Software testing metrics and KPI's

Top 34 Software Testing Metrics & KPIs

Nowadays, quality is the driving force behind the popularity as well as the success of a software product, which has drastically increased the requirement to take effective measures for quality assurance. Therefore, to ensure this, software testers are using a defined way of measuring their goals and efficiency, which has been made possible with the use of various software testing metrics and key performance indicators(KPI’s). The metrics and KPIs serve a crucial role and help the team determine the metrics that calculate the effectiveness of the testing teams and help them gauge the quality, efficiency, progress, and the health of the software testing.

Therefore, to help you measure your testing efforts and the testing process, our team of experts have created a list of 34 critical software testing metrics as well as key performance indicators(KPI’s) based on their experience and knowledge.

The Fundamental Software Testing Metrics:

Software testing metrics, which are also known as software test measurement, indicates the extent, amount, dimension, capacity, as well as the rise of various attributes of a software process and tries to improve its effectiveness and efficiency imminently. Software testing metrics are the best way of measuring and monitoring the various testing activities performed by the team of testers during the software testing life cycle. Moreover, it helps convey the result of a prediction related to a combination of data. Hence, the various software testing metrics used by software engineers around the world are:

  1. Derivative Metrics: Derivative metrics help identify the various areas that have issues in the software testing process and allows the team to take effective steps that increase the accuracy of testing.
  2. Defect Density: Another important software testing metrics, defect density helps the team in determining the total number of defects found in a software during a specific period of time- operation or development. The results are then divided by the size of that particular module, which allows the team to decide whether the software is ready for the release or whether it requires more testing. The defect density of a software is counted per thousand lines of the code, which is also known as KLOC. The formula used for this is: Defect Density = Defect Count/Size of the Release/Module
  3. Defect Leakage: An important metric that needs to be measured by the team of testers is defect leakage. Defect leakage is used by software testers to review the efficiency of the testing process before the product’s user acceptance testing (UAT). If any defects are left undetected by the team and are found by the user, it is known as defect leakage or bug leakage. Defect Leakage = (Total Number of Defects Found in UAT/ Total Number of Defects Found Before UAT) x 100
  4. Defect Removal Efficiency: Defect removal efficiency (DRE) provides a measure of the development team’s ability to remove various defects from the software, prior to its release or implementation. Calculated during and across test phases, DRE is measured per test type and indicates the efficiency of the numerous defect removal methods adopted by the test team. Also, it is an indirect measurement of the quality as well as the performance of the software. Therefore, the formula for calculating Defect Removal Efficiency is: DRE = Number of defects resolved by the development team/ (Total number of defects at the moment of measurement)
  5. Defect Category: This is a crucial type of metric evaluated during the process of the software development life cycle (SDLC). Defect category metric offers an insight into the different quality attributes of the software, such as its usability, performance, functionality, stability, reliability, and more. In short, the defect category is an attribute of the defects in relation to the quality attributes of the software product and is measured with the assistance of the following formula: Defect Category = Defects belonging to a particular category/ Total number of defects.
  6. Defect Severity Index: It is the degree of impact a defect has on the development of an operation or a component of a software application being tested. Defect severity index (DSI) offers an insight into the quality of the product under test and helps gauge the quality of the test team’s efforts. Additionally, with the assistance of this metric, the team can evaluate the degree of negative impact on the quality as well as the performance of the software. Following formula is used to measure the defect severity index. Defect Severity Index (DSI) = Sum of (Defect * Severity Level) / Total number of defects.
  7. Review Efficiency: The review efficiency is a metric used to reduce the pre-delivery defects in the software. Review defects can be found in documents as well as in documents. By implementing this metric, one reduces the cost as well as efforts utilized in the process of rectifying or resolving errors. Moreover, it helps to decrease the probability of defect leakage in subsequent stages of testing and validates the test case effectiveness. The formula for calculating review efficiency is: Review Efficiency (RE) = Total number of review defects / (Total number of review defects + Total number of testing defects) x 100.
  8. Test Case Effectiveness: The objective of this metric is to know the efficiency of test cases that are executed by the team of testers during every testing phase. It helps in determining the quality of the test cases. Test Case Effectiveness = (Number of defects detected / Number of test cases run) x 100
  9. Test Case Productivity: This metric is used to measure and calculate the number of test cases prepared by the team of testers and the efforts invested by them in the process. It is used to determine the test case design productivity and is used as an input for future measurement and estimation. This is usually measured with the assistance of the following formula: Test Case Productivity = (Number of Test Cases / Efforts Spent for Test Case Preparation).
  10. Test Coverage: Test coverage is another important metric that defines the extent to which the software product’s complete functionality is covered. It indicates the completion of testing activities and can be used as criteria for concluding testing. It can be measured by implementing the following formula:
    Test Coverage = Number of detected faults/number of predicted defects.
    Another important formula that is used while calculating this metric is: Requirement Coverage = (Number of requirements covered / Total number of requirements) x 100
  11. Test Design Coverage: Similar to test coverage, test design coverage measures the percentage of test cases coverage against the number of requirements. This metric helps evaluate the functional coverage of test case designed and improves the test coverage. This is mainly calculated by the team during the stage of test design and is measured in percentage. The formula used for test design coverage is: Test Design Coverage = (Total number of requirements mapped to test cases / Total number of requirements) x 100.
  12. Test Execution Coverage: It helps us get an idea about the total number of test cases executed as well as the number of test cases left pending. This metric determines the coverage of testing and is measured during test execution, with the assistance of the following formula: Test Execution Coverage = (Total number of executed test cases or scripts / Total number of test cases or scripts planned to be executed) x 100.
  13. Test Tracking & Efficiency: Test efficiency is an important component that needs to be evaluated thoroughly. It is a quality attribute of the testing team that is measured to ensure all testing activities are carried out in an efficient manner. The various metrics that assist in test tracking and efficiency are as follows:
    • Passed Test Cases Coverage: It measures the percentage of passed test cases.(Number of passed tests / Total number of tests executed) x 100
    • Failed Test Case Coverage: It measures the percentage of all the failed test cases.
      (Number of failed tests / Total number of test cases failed) x 100.
    • Test Cases Blocked: Determines the percentage of test cases blocked, during the software testing process.(Number of blocked tests / Total number of tests executed) x 100
    • Fixed Defects Percentage: With the assistance of this metric, the team is able to identify the percentage of defects fixed.(Defect fixed / Total number of defects reported) x 100
    • Accepted Defects Percentage: The focus here is to define the total number of defects accepted by the development team. These are also measured in percentage.(Defects accepted as valid / Total defect reported) x 100.
    • Defects Rejected Percentage: Another important metric considered under test track and efficiency is the percentage of defects rejected by the development team.(Number of defects rejected by the development team / total defects reported) x 100.
    • Defects Deferred Percentage: It determines the percentage of defects deferred by the team for future releases.(Defects deferred for future releases / Total defects reported) x 100.(Critical defects / Total defects reported) x 100.
    • Critical Defects Percentage: Measures the percentage of critical defects in the software.(Total time taken for bug fixes / Number of bugs).
    • Average Time Taken to Rectify Defects: With the assistance of this formula, the team members are able to determine the average time taken by the development and testing team to rectify the defects.
  14. Test Effort Percentage: An important testing metric, test efforts percentage offer an evaluation of what was estimated before the commencement of the testing process vs the actual efforts invested by the team of testers. It helps in understanding any variances in the testing and is extremely helpful in estimating similar projects in the future. Similar to test efficiency, test efforts are also evaluated with the assistance of various metrics:
    • Number of Test Run Per Time Period: Here, the team measures the number of tests executed in a particular time frame.
      (Number of test run / Total time)
    • Test Design Efficiency: The objective of this metric is to evaluate the design efficiency of the executed test.
      (Number of test run / Total Time)
    • Bug Find Rate: One of the most important metrics used during the test effort percentage is bug find rate. It measures the number of defects/bugs found by the team during the process of testing.
      (Total number of defects / Total number of test hours)Number of Bugs Per Test: As suggested by the name, the focus here is to measure the number of defects found during every testing stage.
      (Total number of defects / Total number of tests)
    • Average Time to Test a Bug Fix: After evaluating the above metrics, the team finally identifies the time taken to test a bug fix.(Total time between defect fix & retest for all defects / Total number of defects)
  15. Test Effectiveness: A contrast to test efficiency, test effectiveness measures and evaluates the bugs and defect ability as well as the quality of a test set. It finds defects and isolates them from the software product and its deliverables. Moreover, the test effectiveness metrics offer the percentage of the difference between the total number of defects found by the software testing and the number of defects found in the software. This is mainly calculated with the assistance of the following formula: Test Effectiveness (TEF) = (Total number of defects injected + Total number of defects found / Total number of defect escaped) x 100.
  16. Test Economic Metrics: While testing the software product, various components contribute to the cost of testing, like people involved, resources, tools, and infrastructure. Hence, it is vital for the team to evaluate the estimated amount of testing, with the actual expenditure of money during the process of testing. This is achieved by evaluating the following aspects:
    • Total allocated the cost of testing.
    • The actual cost of testing.
    • Variance from the estimated budget.
    • Variance from the schedule.
    • Cost per bug fix.
    • The cost of not testing.
  17. Test Team Metrics: Finally, the test team metrics are defined by the team. This metric is used to understand if the work allocated to various test team members is distributed uniformly and to verify if any team member requires more information or clarification about the test process or the project. This metric is immensely helpful as it promotes knowledge transfer among team members and allows them to share necessary details regarding the project, without pointing or blaming an individual for certain irregularities and defects. Represented in the form of graphs and charts, this is fulfilled with the assistance of the following aspects:
    • Returned defects are distributed team member vise, along with other important details, like defects reported, accepted, and rejected.
    • The open defects are distributed to retest per test team member.
    • Test case allocated to each test team member.
    • The number of test cases executed by each test team member.

Software Testing Key Performance Indicators(KPIs):

A type of performance measurement, Key Performance Indicators or KPIs, are used by organizations as well as testers to get data that can be measured. KPIs are the detailed specifications that are measured and analyzed by the software testing team to ensure the compliance of the process with the objectives of the business. Moreover, they help the team take any necessary steps, in case the performance of the product does not meet the defined objectives.

In short, Key performance indicators are the important metrics that are calculated by the software testing teams to ensure the project is moving in the right direction and is achieving the target effectively, which was defined during the planning, strategic, and/or budget sessions. The various important KPIs for software testers are:

  1. Active Defects: A simple yet important KPI, active defects help identify the status of a defect- new, open, or fixed -and allows the team to take the necessary steps to rectify it. These are measured based on the threshold set by the team and are tagged for immediate action if they are above the threshold.
  2. Automated Tests: While monitoring and analyzing the key performance indicators, it is important for the test manager to identify the automated tests. Through tricky, it allows the team to track the number of automated tests, which can help catch/detect the critical and high priority defects introduced in the software delivery stream.
  3. Covered Requirements: With the assistance of this key performance indicator the team can track the percentage of requirements covered by at least one test. The test manager monitors the these this KPI every day to ensure 100% test and requirements coverage.
  4. Authored Tests: Another important key performance indicator, authored tests are analyzed by the test manager, as it helps them analyze the test design activity of their business analysts and testing engineers.
  5. Passed Tests: The percentage of passed tests is evaluated/measured by the team by monitoring the execution of every last configuration within a test. This helps the team in understanding how effective the test configurations are in detecting and trapping the defects during the process of testing.
  6. Test Instances Executed: This key performance indicator is related to the velocity of the test execution plan and is used by the team to highlight the percentage of the total instances available in a test set. However, this KPI does not offer an insight into the quality of the build.
  7. Test Executed: Once the test instances are determined the team moves ahead and monitors the different types of test execution, such as manual, automates, etc. Just like test instances executed, this is also a velocity KPI.
  8. Defects Fixed Per Day: By evaluating this KPI the test manager is able to keep a track of the number of defects fixed on a daily basis as well as the efforts invested by the team to rectify these defects and issues. Moreover, it allows them to see the progress of the project as well as the testing activities.
  9. Direct Coverage: This KPI helps to perform a manual or automated coverage of a feature or component and ensures that all features and their functions are completely and thoroughly tested. If a component is not tested during a particular sprint, it will be considered incomplete and will not be moved until it is tested.
  10. Percentage of Critical & Escaped Defects: The percentage of critical and escaped defects is an important KPI that needs the attention of software testers. It ensures that the team and their testing efforts are focused on rectifying the critical issues and defects in the product, which in turn helps them ensure the quality of the entire testing process as well as the product.
  11. Time to Test: The focus of this key performance indicator is to help the software testing team measure the time that a feature takes to move from the stage of “testing” to “done”. It offers assistance in calculating the effectiveness as well as the efficiency of the testers and understanding the complexity of the feature under test.
  12. Defect Resolution Time: Defect resolution time is used to measure the time it takes for the team to find the bugs in the software and to verify and validate the fix. Apart from this, it also keeps a track of the resolution time, while measuring and qualifying the tester’s responsibility and ownership for their bugs. In short, from tracking the bugs and making sure the bugs are fixed the way they were supposed to, to closing out the issue in a reasonable time, this KPI ensures it all.
  13. Successful Sprint Count Ratio: Though a software testing metric, this is also used by software testers as a KPI, once all the successful sprint statistics are collected. It helps them calculate the percentage of successful sprints, with the assistance of the following formula: Successful Sprint Count Ratio: (Successful Sprint / Total Number of Sprints) x 100.
  14. Quality Ratio: Based on the passed or failed rates of all the tests executed by the software testers, the quality ratio, is used as both a software testing metrics as well as a KPI. The formula used for this is: Quality Ratio: (Successful Tests Cases / Total Number of Test Cases) x 100.
  15. Test Case Quality: A software testing metric and a KPI, test case quality, helps evaluate and score the written test cases according to the defined criteria. It ensures that all the test cases are examined either by producing quality test case scenarios or with the assistance of sampling. Moreover, to ensure the quality of the test cases, certain factors should be considered by the team, such as:
    • They should be written for finding faults and defects.
    • Test & requirements coverage should be fully established.
    • The areas affected by the defects should be identified and mentioned clearly.
    • Test data should be provided accurately and should cover all the possible situations.
    • It should also cover success and failure scenarios.
    • Expected results should be written in a correct and clear format.
  16. Defect Resolution Success Ratio: By calculating this KPI, the team of software testers can find out the number of defects resolved and reopened. If none of the defects are reopened then 100% success is achieved in terms of resolution. Defect resolution success ratio is evaluated with the assistance of the following formula: Defect Resolution Success Ratio = [ (Total Number of Resolved Defects) – (Total Number of Reopened Defects) / (Total Number of Resolved Defects) ] x 100.
  17. Process Adherence & Improvement: This KPI can be used for the software testing team to reward them and their efforts if they come up with any ideas or solutions that simplify the process of testing and make it agile as well as more accurate.


Software testing metrics and key performance indicators are improving the process of software testing exceptionally. From ensuring the accuracy of the numerous tests performed by the testers to validate the quality of the product, these play a crucial role in the software development lifecycle. Hence, by implementing and executing these software testing metrics and performance indicators you can increase the effectiveness as well as the accuracy of your testing efforts and get exceptional quality. 

Contact our Experts to Build KPI’S for your Project.

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types of bugs in software testing

16 Types of Bugs in Software Testing

Software development is a long and ongoing process for a developer. Merely writing the code and believing that it would work is not the way developers intend on creating reliable software. The software development life cycle includes planning, analyzing, designing, developing, testing, implementing, and maintaining the software.

Even though every action in the process is equally significant, developers spend the most time in the testing phase. Presently, due to the rising competition in the industry, developers have to create the software in the least possible time. With that in mind, they try to become as efficient as possible so that they can stay ahead of others. However, there is one part of the software development process which should not be done in a rush which is software testing.

types of bugs in software testing

Developers spend innumerable hours in software testing to find out bugs that could eventually degrade the software quality. Sometimes these bugs can be as simple as an unresponsive button whereas they can become highly severe where bugs could make the entire software unresponsive. In both ways, it is the user experience that will be affected and would lead to monetary losses of the organization. Considering that fact, software testing has become an integral part of every SDLC. This article will explain types of bugs in software testing.

What is a Software Bug?

While writing a software code, the developer could make a mistake that could hamper the functioning of a certain feature in the software. This mistake or error is the bug that will cause the software to malfunction. The reason why such errors are called a bug is because of an issue faced by Grace Murray Hopper, a renowned personality in computing history. While working on an electromechanical computer, he faced an issue that affected the performance of the computer. While looking for the issue, he found a moth stuck inside the computer. Since then, every error in the software code or a computer system is called a bug.

Different Types of Bugs in Software Testing:

No matter the software type, software bugs are categorized into three types; Nature, Priority, and Severity. Classification of bugs in software testing is done on the basis of their nature and impact on the user experience.

  1. Software Bugs by Nature:
    Software bugs have different natures where they affect the overall functioning of the software differently. Though there are dozens of such bugs existing currently, you may not face them frequently. With that in mind, here are the most common software bugs categorized by nature that you are most likely to witness at some point in your software development career.

    • Performance Bugs:
      No user wants to use software with poor performance. Software bugs that lead to degraded speed, stability, increased response time, and higher resource consumption are considered performance bugs. The most significant sign of any such bug in software is by noticing slower loading speed than usual or analyzing the response time. If any such sign is found, the developer may begin diagnosing a performance bug. The performance testing phase is part of the development process where every such bug is detected in the software.
    • Security Bugs:
      While using software, security is the biggest concern of a user. Software with poor security will not only put the user’s data at risk but will also damage the overall image of the organization which may take years to recuperate. Due to their high severity, security bugs are considered among the most sensitive bugs of all types. Though it is self-explanatory, security bugs may make the software vulnerable to potential cyber threats. Sometimes, the software organization may not notice such attacks whereas in some cases, these attacks could cause monetary loss to the users, especially small and medium-scale businesses. XSS vulnerabilities, logical errors, and encryption errors are some of the commonest security bugs found in the software. Developers put special focus on checking the code to find any underlying security bug to minimize the risk of cyber-attacks.
    • Unit Level Bugs:
      Unit level bugs are fairly common in software development and do not cause much damage to it as well. Facing basic logic bugs or calculation errors are considered unit-level bugs. The testing team along with the agile team test a small part of the code as a whole. The reason why this testing method is preferred is to make sure that the entire code is working as it is meant to. While testing, the team may encounter unit-level bugs which can be fixed easily as the team is only working with a small code.
    • Functional Bugs:
      Software is as good as the feature it provides. If any of the functionality of a software is compromised, the number of users will start to decline drastically until it becomes functional again. A functional bug is when a certain feature or the entire software is not functioning properly due to an error. The severity of such bugs depends on the feature they are hampering. For instance, an unresponsive clickable button that is not functioning is not as severe as the entire software not working. Functional testing is done by the testing team to identify any such software bug causing functionality errors. Once identified, the team decides its further classification and severity.
    • Usability Bugs:
      Probably one of the most catastrophic bugs for software, a usability bug or defect can stop the software from working to its potential or make it entirely unusable. Examples of this bug in software testing are the inability to log in to the user account or the inefficient layout of the software for the user. The bottom line is that this type of defect or bug can make it complex for the user to use the software efficiently. The developers and engineers have to look out for the right usability requirements while testing the code to identify such bugs.
    • Syntax Errors:
      Syntax errors are among the commonest software bug types and do not allow the application to be compiled appropriately. This bug occurs due to an incorrect or missing character from the source code due to which the compiling will be affected. A small error like a missing bracket could lead to this problem. The development or testing team will get to know about this bug during compiling and will further analyze the source code to fix the missing or wrong characters.
    • Compatibility Errors:
      Whenever a software or an application is not compatible with hardware, or an operating system, it is considered as incompatible software or a compatibility error. Finding a compatibility error is not a common practice as they may not show up in the initial testing. Due to this reason, the developers should go for compatibility testing to make sure that their created software is compatible with common hardware and operating systems.
    • Logic Bugs:
      Another one of the most frequently found bugs in a software code, logic errors make the software give wrong output, software crash or failure. In the majority of cases, these bugs are caused due to coding errors where it may make the software stuck in a never-ending loading loop. In that case, only an external interruption or software crashing are the two only things that can break the loading loop.
  2. Priority-Based Software Bugs:
    The foremost category here is priority-based software bugs. These are based on the impact these bugs leave on the business. Here, the developers will analyze the bug to determine its impact and its defect priority. Afterward, the timeline is given to each bug where it should be rectified within the stipulated time frame to minimize the bug effect on the user. Here are the four types of priority-based software bugs.

    • Low-priority defects:
      Low priority defects do not cause much impact on the functioning of the application. Rather, they are more about software aesthetics. For instance, any issue with the spelling or the alignment of a button or text could be a low-priority defect. The software testing will move to the exit criteria even if the low-priority defects are not fixed, but they should be rectified before the final release of the software.
    • Medium-priority defects:
      Akin to low-priority defects, medium-priority defects do not cause any significant impact on the software, but they should be fixed in any subsequent or upcoming release. Such defects may not have the same effect for every user and it may vary with the device as well as specific configuration they have.
    • High-priority defects:
      Unlike the previous two, the exit criteria of high-priority defects are not met until the issue is resolved. Every bug falling in this category may make certain features of the software unusable. Even though it may not affect every user, it is mandatory to fix these bugs before any further step is taken in software development or testing.
    • Urgent Defects:
      As the name suggests, all bugs that should be dealt with utmost urgency fall under this category. Urgent defects may leave a lasting impact on the brand image as well as affect the user experience drastically. The stipulated timeline for fixing these bugs is within 24-hours of reporting.
  3. Software Bugs by Severity:
    Depending on the technical effect that the bug will cause on the software, the bugs are categorized into four categories.

    • Low Severity Bugs:
      Low severity bugs do not cause much damage to the functioning of the software as their primary target is the user interface. For instance, the font of the text on the program differs from what was used. These bugs can be fixed easily and are nothing to worry about.
    • Medium Severity Bugs:
      Every bug that can affect the functionality of the software a little bit is considered a medium severity bug. All such bugs make the software function different from what it is supposed to function. Though they are not also major for the program, they should be fixed for a better user experience.
    • High Severity Bugs:
      High severity bugs affect the software functionality, making it behave differently from what it was programmed for. Not only are such bugs damaging for the software, they sometimes make the entire software unusable for the user.
    • Critical Bugs:
      Critical bugs are the most damaging bugs in the category that can hinder the functionality of the entire software. The reason why critical bugs are considered the most damaging is that further testing on the software becomes impossible till such bugs exist in the software.

How to Find Underlying Software Bugs?

Finding a software bug is a daunting task that should be done to ensure that the software functions as it should. However, the question is how to find software bugs. To help developers in accomplishing that task, here are some of the ways to find bugs in software.

  1. Use Test Cases:
    Test cases are among the foremost things that will help a developer in identifying bugs in the software. Every developer should prepare excellent test cases prior to their testing including functional test cases that will help them in analyzing the risk of the application and how it will perform under different circumstances.
  2. Test on Devices:
    Sometimes all developers do is test the code in a virtual machine, leaving behind real devices. In some cases, that approach may work, but this practice is ineffective in large-scale software. With that in mind, developers should expand their testing reach and test the software on multiple real devices. Doing so will not only help them in understanding how the software performs on different configurations, but will also help you in knowing its compatibility as well.
  3. Use Bug Tracking Tools:
    Bug tracking tools are probably the easiest way to identify bugs in software. Such tools aid in tracking, reporting, and assigning bugs in software development, making testing easier. Several such tools like SpiraTeam, Userback, and ClickUp are available that can accomplish this task and make software testing uncomplicated to much extent.


100 % eradicating bugs from software is practically inevitable. With every new update release or new software, some bugs will come along. Rather than looking for every bug and fixing it, developers and testers analyze the severity of the bug and determine whether fixing the bug is worth the effort or not. ThinkSys Inc performs rigorous software testing to find complex bugs in software. Furthermore, our software testers create a strategy to prioritize bugs that will help in making the software better with each update. The motive is to identify errors in an early stage so that the software can reach the release date quickly and error-free.

With that in mind, if you believe that you need professional consultation regarding software testing, you can always reach out to our QA Experts.

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saas performance testing

SaaS Performance Testing: Has Performance Testing Changed?

As enterprises increasingly realize the benefits of Software as a Service (SaaS), its use over on-premise applications continues to gain momentum globally. These cloud-based models have a lot to offer to businesses, as they operate on a rental model, foregoing the need for hardware installation. SaaS brings with it, easy access to high-end operational modules, affordability, scalability, and ease of doing business. All of this has led to a huge rise in the use of SaaS across industries, and business niches. The shift to cloud-based products rose sharply during the pandemic as enterprises sought the greater flexibility, agility, and resilience the cloud provided.

In a study, Gartner forecasts that end-user spending on public cloud services was all set to reach $396 billion in 2021 and now it will grow another 21.7% to reach $482 billion in 2022.

Like all good things, SaaS too comes with its bundle of complexities that include its systems, operational aspects, and application stacks. A good number of SaaS providers are known to have reported unsatisfied customers. The demand for sleek functionality and usability, consistent reliability, and security are among the top needs of most such disgruntled users. Among the biggest challenges in the cloud world is the difficulty of predicting and preparing for sudden surges and sharp falls in the number of users and usage. It is, therefore, imperative to put in place an appropriate SaaS Performance testing and management strategy to deal with these and other allied challenges.

saas performance testing

Goal of SaaS Performance Testing, and How it is different?

SaaS performance testing can be a complicated affair as it calls for specialized test planning that can be done only when the service provider understands the testing process along with the tools used to carry it out. The focus of any SaaS cloud performance testing is to check 3 key factors:

    • Speed – to establish that the application is fast;
    • Scalability – to determine the maximum user-load ability of the application;
    • Stability – to verify if the application performs steadily under varying loads;

Here lies the difference between conventional testing for software and performance testing for SaaS applications. The goal of performance testing for SaaS is not just to find bugs, or glitches, but to remove any performance bottleneck that might hinder application efficacy in congruence with the business’s goals and objectives.

Performance Testing particular to Cloud Applications:

Coming down to the ground level of cloud performance testing, here are the specific types that are carried out to ensure optimal application functioning:

      • Load testing to ascertain multiple user optimal performance;
      • Capacity testing to identify and benchmark the maximum traffic that the system can handle optimally;
      • Stress testing to determine how well the system responds to increased or maximum intended traffic;
      • Soak testing to measure system performance when it is exposed to heavy traffic for extended periods;
      • Failover testing to verify the system’s ability to call in additional resources under heavy traffic conditions to ensure end users’ experience is not affected;
      • Browser testing to determine its compatibility with the system;
      • Latency testing to measure time lapsed for data messages to move between different points in the cloud network;
      • Target infrastructure-testing to check each component and layer of the application by isolating and testing it for its ability to deliver required performance levels;

Concerns of SaaS Performance Testing:

Taking a close look at the way performance testing has changed for this genre of applications, one sees that there are 3 different layers of concern to it:

      • Before-release-concerns: The growing popularity of SaaS is due to its quick scalability according to the number of users at a given time. SaaS providers use auto-scaling cloud tools that save money and time that launch the cloud resources as and when they are needed and terminate them as soon as they’re not in use. .
      • After-release-concerns: Running frequent checks on updates and upgrades and installing the latest versions is a major concern as the developing company releases several of them in a year, and at times, every month. The testing process also includes fixing all software defects as reported by subscribers. Performance testing here is then to validate new features’ smooth operation, fixing bugs, and smooth functioning of all functions after the fix.
      • Before-and-after-release concerns: Even though the preference for multi-tenant architecture remains high with service providers as it saves finance, it brings along several concerns with it. It is crucial for service providers to test and ensure that critical business data is not shared with others using the same cloud resources, or by different end-users. Ensuring that there is no business-data leak is an important testing concern.

These apart, there is the major concern of API flexibility to satisfy the integration needs of new subscribers or make the solution compatible with the users’ preferred versions of web browsers. Configuration and customization testing further extend to the realm of tuning unified SaaS solutions to subscribers’ specific business logic. Customizations made at the time of installation may get damaged during the bug-fixing process. Performance testing then includes making the right amendments to keep the solution optimally running as per business needs.

Summing it up

Performance testing is integral to consistent system reliability, smooth functionality, and apt usability. When it comes to SaaS cloud applications, performance testing and management are different from those applied for conventional software solutions. The shift in the focus of testing is mainly on the areas that determine the flawless functioning of the application for the entire spectrum of conditions that end users are likely to subject the system to. In addition, there are concerns of upgrades, bug-fixing, and smooth customization functioning after such processes. These have led to a paradigm shift in the world of performance testing, its tools, and the processes used.

Connect with us to know just how to ensure your SaaS product is tested and ready for the real world.

testing serverless apps

Challanges in Testing Serverless App Testing

Serverless approaches have become a boon for businesses looking for ways to rid themselves of infrastructure management complexities that burden traditional application development. With serverless, you may simply “throw” the code on the cloud and get a functioning app. However, implementing serverless applications brings along certain complexities as well.

The tight integration with the cloud plus the inability to directly access things required for code execution makes it difficult for businesses to control their work environment when they choose to go serverless. There are many implications and complexities here that have a direct impact on testing strategies.

Onboarding serverless app development will, first of all, require you to understand how serverless applications’ testing differs from traditional app testing and how can you adapt to manage the new ways.

The key challenge of going serverless is that you may not find the right infrastructure to maintain it. If you don’t have a native infrastructure, there’s no machine from where admins can fetch the logs. Hence, when you discover an app deficiency, you cannot expect that your system admin will provide you with the required logs as the logs don’t exist at all. You will need a practical approach for serverless applications testing. Take a look at the theoretical app and dive into the details of testing it for a better understanding.

testing serverless apps

4 Challenges of Serverless App Testing:

Serverless solutions and technologies are expanding rapidly – blurring the differences between infrastructure and the cloud. It’s changing the methodology of app development in many cases. With countless benefits of eliminating the inherent server infrastructure that serverless technologies offer, there are several complexities and hidden challenges as well that come along. Let us explore the complexities associated with serverless solutions.

Challange#1: Relatively new technology:

Since serverless is new, businesses face the challenge of finding skilled people and resources to implement serverless technologies efficiently, effectively, and accurately. Serverless is a novel cloud-based technology that has, currently, a relatively smaller knowledge base (at least now). This introduces more challenges in serverless technology adoption considering the difficulty of undertaking and testing tasks such as designing, architecting, troubleshooting, developing, etc. with serverless components. ISVs turning to serverless are looking to do this with the help of skilled partners with relevant experience in the development, troubleshooting, and implementation of serverless technologies and solutions.

Challange#2: Reduced control – 

A persistent argument against resources migration to the cloud is that it may result in loss of control compared to on-premises environs. This is applicable in the case of serverless technologies as well. When vendors and businesses use the public cloud resources, there’s a certain degree of renounced control of applications, infrastructure, services, or data housed there. Of course, many of these strong objections have already been addressed mainly because cloud environments and providers have matured now. In point of fact, modern businesses cannot survive without the cloud because it has become a vital part of enterprise services and infrastructure. Public cloud, over the years, has come of age and much of that has rubbed off on serverless application development too. The benefits of using serverless capabilities and functions in public cloud environments and other public cloud offerings strongly outweigh the visible loss of control on the infrastructure. Your data and system are your responsibility just as it is with any other infrastructure or system kept in the public cloud. In other words, ISVs must properly architect the serverless systems. The approach must focus on fault tolerance and high availability, hence regaining some control over the architecture via software design. With the help of multiple cloud regions meant for serverless architecture, ISVs can offer resilience from potential outages likely to occur in cloud provider networks and infrastructure.

Challenge #3: Vendor lock-in: 

Serverless is a relatively new offering for a vast majority of public cloud vendors. The exact specifications and standardization of serverless are only now being determined. As each vendor is developing and updating their serverless offerings bearing in mind what the technology should offer, this may result in vendor lock-in when you choose one serverless offering than the other. That said, there are multiple serverless platforms already on offer from most of the top cloud players, making the choice wider and deeper.

Challenge #4: Integration Testing Challenges: 

Testing solutions and platforms is vital for any production enterprise solution & development methodology. Compared to traditional systems’ development, the testing and tooling of serverless components can be highly challenging to manage. Performing integration testing is more difficult with serverless systems as they are instantiated as ephemeral, trigger-based-code. In integration testing, individual components are combined and tested as a group rather than individually. As a result, issues are exposed with the interaction between existing components in the system. Serverless’ transient existences and abstractions make it even more challenging. The vendors that have been utilizing traditional ways of integration testing need to adapt to newer challenges of integration testing involving serverless components. Serverless function-code may hugely benefit from the designing code that utilizes hexagonal architecture of decomposing the function code through the layers-of-responsibility. Integration testing of serverless code with these and several other design techniques may empower ISVs to perform integration testing (that involves serverless code) successfully.

Final Thoughts:

Serverless testing has many hidden challenges and these include the technology’s newness, renounced control, multi-tenancy & resource limits, testing challenges, startup time, and vendor lock-in among others. Businesses including vendors must skillfully craft their serverless apps for considering these challenges. This should facilitate harnessing the immense capabilities of serverless architecture while minimizing the inherent challenges of serverless offerings. Talk to us to know how to craft robust testing strategies for your serverless apps.

Talk to Us For Serverless Apps Testing

Related Blogs:

  1. Exploring Serverless Technologies.

  2. Serverless Computing Options.

  3. AWS and Azure Business Enablers 2022.

Enterprise API Testing

A Closer Look At API Testing As The Buzz Grows Around The API Enterprise

An introduction to APIs

As every programmer now knows, an Application Programming Interface (API) is a computing interface that allows data exchange and communication between two different software systems. An API defines how the two software systems can interact, the type of requests to be made, how to make the requests, data formats to be used, etc.

As enterprises become focused on integration and simplification of the enterprise tech ecosystem, APIs are back in focus as is their testing.

Enterprise API Testing

Examples of API use-cases that we use in our Daily Lives

From logging in to any social media platform to performing a simple google search, everyone has accessed API integration somewhere or the other. Enterprises are building an array of applications and solutions on such use-cases. For relatability, here are five common examples of API usage in our daily lives.

  • Login using XYZ account: It is very convenient to visit any new website and find the functionality to log in with Facebook, Google, GitHub, or other pre-existing accounts. This feature also relies upon APIs to not pose a security threat to your accounts. The applications with this feature simply rely upon APIs to authenticate the user with each login through identification information.
  • Weather snippets: Another common API usage is checking the weather data. Users simply look for weather + specific place, and the search result finds a dedicated box at the top (a rich snippet) with the weather forecast. Since Google does not collect weather data itself, this forecast is outsourced from a third party with the help of APIs. The weather APIs send them data that is easy to reformat. Currently, Google uses data from The Weather Channel.
  • Travel booking: It is easy to get flabbergasted by the deals and cheap flight options available on travel booking sites. But all this data is also extracted using third-party APIs to collect hotel and flight availability details from providers. APIs help machines automatically exchange data and requests, in the lack of which, the entire process of travel booking used to be manual.
  • FX brokerages and trading: Investments have grown in the digital world, and several applications have come up with APIs to help with trading. With access to multiple FX markets, APIs facilitate algo-trading strategies and allow access to live-streaming prices, trade execution, and advanced order types.
  • Pay with PayPal: PayPal or other payment merchants are directly embedded within eCommerce stores, nowadays. This functionality is also supported by APIs that ensure the end application only accesses the information that it requires and does not acquire unintended permissions. The API also comes into play to send confirmation of payment back to the application.
  • Bots: Social media bots are powered by APIs. Users can use these bots to send hourly reminders, identify grammatical errors, get tweets when Netflix releases new content, and also get reminders of new activity on their own Twitter accounts.

What is API Testing?

API testing is testing that validates the functionality of API. As the name suggests, API testing checks the overall functionality, performance, reliability, and security of the programming interfaces. API testing does not look at the overall appeal and presentation of the application like GUI testing. It focuses on the business logic strata of the software architecture and is often performed at the message layer.

Classes of Web API: SOAP and REST

There are two wide divisions of web service for Web API – SOAP and REST.

Simple Object Access Protocol (SOAP) is defined by the W3C standards, a standard protocol to send and receive web service responses and requests.

Representational State Transfer (REST), like HTTP, is a web standards-based architecture. There is no official standard for REST Web APIs.

API Testing Approach

API testing follows a predefined methodology, once the build is ready. This testing may not even require the source code. The following are tested in API testing:

  • Understand the functionality of an API.
  • Define the input parameters.
  • Verify how the error codes are handled by the API.
  • Keys verification.
  • Test case to perform XML, JSON schema validation.
  • Validate the keys with a range of minimum and maximum APIs.

Apart from the usual SDLC process of testing, API testing should also cover documentation, automated testing, security testing, usability testing, and discovery testing.

Bugs detected in API Testing

The common bugs detected during API testing are:

  • Security issues.
  • Missing or duplicate functionalities.
  • Performance discrepancy.
  • Incorrect handling of valid argument values.
  • Incorrect structuring of response data (JSON or XML).
  • Multi-threading issues.
  • Unused flags.
  • Failure in handling error conditions gracefully.
  • Failure in establishing a reliable connection with the API.
  • Improper errors.

Challenges of API Testing

The major challenges in API testing include:

  • Tracking API inventory and keeping up with the updates.
  • Thorough knowledge and understanding of business logic and rules.
  • Complex contracts or protocols for API interaction.
  • Testing enormous data and keeping it reusable.
  • Testers should have coding knowledge.
  • Testers should also know parameters selection and categorization.
  • Validation and verification of output in a different system.
  • No GUI available to test the application.
  • Parameter Combination, Parameter Selection, and Call Sequencing pose as main challenges in Web API testing.
  • Exception handling function should be tested.


The Solution to all These Problems:

APIs are everywhere in our daily digital lives. To succeed in the digital sphere, most organizations are now integrating APIs into their existing system strategies. However, appropriate API testing continues to be a challenge.

The ThinkSys API testing services stand apart as an integral part of API development and integration. Their reliable services guarantee the best security and compliance testing. From performance to functionality, they focus on every core aspect of an API and ensure maximum risk coverage to improve productivity.

React Testing Library Complete guide 2021

React Testing Library Complete Guide 2021

Among the various front-end development libraries, React is an important testing library and is frequently used by developers to build seamless and quality products. From enabling clear programming to being backed up by a strong community, this open-source JavaScript library helps deliver fast performance. However, these benefits of the software or applications are not only a result of better and clear programming.

Testing also plays an integral part in validating the quality of the product as well as its speed. Currently, numerous frameworks are used to test React components, such as Jest, Enzyme and React-Testing-Library. Though the former two are well renowned among testers, React Testing Library is steadily gaining momentum, due to the various benefits it offers to the testing team, and it is this method of testing React components that we are going to discuss in detail today, to further understand its significance.

React Testing Library Complete guide 2021

What is React Testing Library? (Click Here to Tweet)

Introduced by Kent C. Dodds, React Testing-Library is a lightweight solution for testing React components and is commonly used in tandem with Jest. React Testing Library came into being as a replacement to Enzyme and is now encouraging better testing practices, by providing light utility functions on top of react-dom and react-dom/test-utils. It is an extremely beneficial testing library that enables testers to create a simple and complete test harness for React hooks as well as to easily refactor code going forward.

The main objective of this library is to provide a testing experience that is similar to natively using a particular hook from within a real component. Moreover, it enables testers to focus directly on using the library to test the components and assert the results. In short, React Testing Library guides testers to think more about React testing best practices, like selectors and accessibility rather than coding. Another reason that makes it helpful is that this library works with specific element labels of the React component and not the composition of the UI.

Want to get a better insight into the working of React Testing Library? Check out the React Testing Library examples here.

Key Points of React Testing Library:

From supporting new features of React to performing tests that are more focused on user behavior, there are numerous features of React Testing Library that make it more suitable for testing React components than others.

Some of these features are:

  • It takes away excessive work required to test React components well.
  • It is backed up as well as recommended by the React community.
  • It is not React specific and can be used with Angular and other languages.
  • It enables testers to write quality tests that ensure complete accuracy.
  • Encourages applications to be more accessible.
  • It offers a way to find elements by a data-testid for elements where the text content and label don’t make sense.
  • Avoids testing the internal component state.
  • Tests how a component renders.

The Guiding Principles of React Testing Library:

The guiding principle of this library is the more the tests resemble the way the software is used the more confidence they can give the testing team. To ensure this, the tests written in React Testing Library closely depict the way users use the application. Other guiding principles for this testing library are:

  • It deals with DOM nodes rather than component instances.
  • Generally useful for testing individual React components or full React applications.
  • While this library is focused on react-dom, utilities are included even if they don’t directly relate to react-dom.
  • Utility implementations and APIs should be simple and flexible.

Why React Testing Library is required?

React Testing Library is an extremely beneficial testing library and is needed when the team of testers wants to write maintainable tests for React components, as well as when there is a need to create a test base that functions uniformly even when refactors of the components are implemented or new changes are introduced. However, the use of the React Testing Library is not limited to this. As this library is neither a test runner or framework nor is it specific to a testing framework, it is also used in the following two circumstances:

  • In cases when the tester is writing a library with one or more hooks that are not directly tied to a component.
  • Or when they have a complex hook that is difficult to test through component interactions.

Tests Performed While testing React Components:

There are various tests for your React component or React application testing that ensures that they deliver the expected performance. Among these, the following are the most crucial tests performed by the team and are hence discussed in detail:

  1. Unit Testing:
    An integral part of testing React components, unit testing is used to test the isolated part of the React application, usually done in combination with shallow rendering as well as functional testing React components. This is further executed with the assistance of an important technique of front-end unit testing react component, snapshot testing.

Snapshot Tests:

Another testing technique used to test React components in React Testing Library snapshot testing, wherein the team takes a snapshot of a React component and compares it with later versions to validate that it is bug-free, runs accurately and depicts expected user experience. The main objective of Snapshot testing is to make sure the layout of the component didn’t break when a change was implemented.

Snapshot testing is suitable for React component testing as it allows the testing team to view the DOM output and create a snapshot at the time of the run. Moreover, this testing technique is not limited to testing implementation details or React testing library hooks and is used with other testing libraries and frameworks, like Jest, as it enables testing of JavaScript objects.

  1. Integration Tests:
    One of the most important tests performed to test React components, Integration Testing, ensures that the composition of the React components results in the desired user experience. Since writing React apps is all about composing components, Unit Testing React with Jest alone is not suitable for ensuring that the app, as well as the components, are bug-free. Integration tests validate whether different components of the app work or integrate with each other by testing individual units by combining and grouping them.
  2. End-to-End Testing:
    Performed by combining testing library React and Cypress or any other library or frameworks, end-to-end testing is another important step of the testing activities. It helps ensure that the React app works accurately and delivers the necessary functionality expected by the users. This test is a multi-step that combines multiple units and integrates the tests into one huge test.

Other Important Tools & Libraries:

Though React-Testing-Library is a prominent library for testing React components, it is not the only library out there. There are various other React testing tools and libraries used by the team of testers to verify the quality and accuracy of React components. A few of these are mentioned below:

  1. Jest: Adopted by large scale organizations like Uber and Airbnb, Jest is among the most popular frameworks and used by Facebook to test React components. It is also recommended by the React team, as its UI snapshot testing and complete API philosophy combines well with React.
  2. Mocha: One of the most flexible Javascript testing libraries, Mocha, just like Jest and other frameworks can be combined with Enzyme and Chai for assertion, mocking, etc. when used to test React. It is extremely configurable and offers developers complete control over how they wish to test their code.
  3. Chai: Another important library used for testing components, Chai is a Behavior Driven and Test Driven Development assertion library that can be paired with a JavaScript testing framework.
  4. Karma: Though not a testing framework or assertion library, Karma can be used to execute JavaScript code in multiple real browsers. It is a test runner that launches an HTTP server and generates HTML files. Moreover, it helps search for test files, processes them and runs assertions.
  5. Jasmine: A Behavior Driven Development (BDD) testing framework used for JavaScript tests, Jasmine, is used to test the React app or components. It does not rely on browsers, DOM, or any JavaScript framework and is traditionally used in various frameworks like Angular. That’s not all, Jasmine consists of a designated help util library that is built to make the testing workflow smoother.
  6. Enzyme: One of the most common frameworks usually discussed along with React Testing Enzyme is not a testing framework, but rather a testing Utility for React that enables testers to easily test outputs of components, abstracting the rendered component. Moreover, it allows the team to manipulate, traverse, and in some cases stimulate runtime. In short, it can help the team React test render components, find elements, and interact with them.
  7. React Test Utils and Test Renderer: Another collection of useful utilities in React, React test renderer is used in identifying and throwing an error using any testing library Jest Dom for example. React-test-renderer typescript enables the team to render React components into pure JavaScript objects without depending on DOM. It can support the basic functionality needed for testing React components and offers advantages that it is in the same repository as the main React package and can work with its latest versions.
  8. Cypress IO: A JavaScript end-to-end testing framework, Cypress is easy to set-up, write, and debug tests in the browser. It is an extremely useful framework that enables teams to perform end-to-end React application testing, while simultaneously making the process easy. It has a built-in parallelization and load balancing, which makes debugging tests in CI easier too.


Testing, be it for a React component, application or software, is crucial to validate the quality, functionality, as well as UX & UI. React Testing Library is among the various testing frameworks that are helping testers create apps that are suitable for users worldwide. From remarkable React testing library accessibility to a scalable React test environment, label text features, and more, this front-end testing framework offers a wide range of advantages, which is making it popular among testers. So, whether you are using the Jest test function or React testing library, testing React components and applications is easier with all.

Want to understand the scope of React Acceptance Testing? Click here.

Book your Free Reactjs Testing POC Today

role of regression in agile development

The Special Role of Regression Testing in Agile Development

Presumably, everyone here who has developed products knows that regression testing is done to validate the existing code after a change in software. Unlike most other testing, it validates that nothing got broken in the already existing functionality of the software product even as changes were made to other parts. In a nutshell, the aim is to confirm that the product isn’t affected by the addition of any new features or bug fixes. Often, older test cases are, re-executed for reassurance that there were no ill-effects of changes.

Regression testing is necessary for all product development where the product is evolving, that is, in effect for all products!

Which Brings is to Agile Software Development?

The Agile method calls for rapid product iterations and frequent releases. Obviously, this includes shorter and more frequent testing cycles. This is to ensure that the quality of the output of the sprints is intact whenever the software is released. These constant churns call for a massive focus on regression testing.

A sound regression testing strategy mainly helps the teams focus on new functionalities and maintain stability as the product increments take place. It makes sure that the earlier release and the new code are both in-sync. This is how the software’s functionality, quality, and performance remain intact even after going through several modifications.

To put things into perspective – the Agile method is all about iterative development and regression testing is all about focusing on the effects that occur due to that iterative new development.

What Makes Regression Testing Special in Agile Development?

  • Helps Identify Issues Early– One of the ways in which Agile teams build their regression testing strategy is to identify the improvements or the error-prone areas and gather all the test cases to execute for those cases. This preparation helps them gear up for the accelerated tests and also, prioritize the test cases. This way they can target the product areas that need more focus on quality. Additionally, by detecting defects early in the development cycle, regression testing can help reduce excessive rework. This helps release the product on time.
  • Facilitates Localized Changes – Regression testing makes it possible for development teams to confidently carry out localized changes to the software or sometimes, even for bigger changes. The teams mainly focus on the functionality that they planned for the sprint secure in the knowledge that the regression tests will highlight the areas that are affected by the most recent changes across the codebase.
  • Business Functionality Continuity – Since regression testing usually takes into consideration various aspects of the business functions, it can cover the entire system. The aim is to run a series of similar tests repeatedly over a period of time in which the results should remain stable. For each sprint, this helps test new functionality and it makes sure that the entire system continues to work in an integrated manner and the business functionality continues in the long run.
  • Errors Are Reduced to a Large Extent – The thing with an Agile development environment is that there is a reduced scope for errors during the accelerated release cycles. The series of regression tests at each level of the release ensures that the product is robust and resistant to bugs. This helps in enhancing the software’s stability and improves its overall quality.
  • Offers Scope to Add Better Functionalities – Introducing new functionalities in any application can be time-consuming because there are several aspects that need to be taken into consideration. This process becomes less cumbersome with Agile development, which can boost gradual changes. Regression tests amp up the power of the methodology by giving the scope of introducing several functionalities in seamlessly.
  • Quicker Turnaround – There are multiple tools for regression testing. It’s also possible to automate significant portions of the regression testing given the repetitive nature of the tests. This offers the Agile development team faster feedback. They can achieve faster turnarounds and can accelerate releases confidently.

To Sum Up:

Regression testing is a staple while developing a well-integrated, robust software as it evolves. In the accelerated Agile environment, it helps ensure that any newly developed sprint has no adverse effect on the existing code or functionality of the business. Furthermore, a carefully considered regression testing strategy helps the Agile teams be confident that every feature in the software is in perfect condition with all the updates and fixes required. It’s the insurance policy that Agile product development teams need.

Where AI could fall short in Software Testing

Where AI Could Fall Short In Software Testing?

We have written earlier how Artificial Intelligence can increase the efficiency and speed of software product development. Now that AI in software development is gaining acceptance, let’s look at how AI can play out in software testing- its potential as well as shortcomings.

After test automation, AI-based testing looks like the obvious next step. Here’s how things have rolled out in the software testing space:

  • Traditionally, manual testing has always had a role to play, because no software is produced sans bugs. Even with all the tools available, a key part of the process is handled manually by specialized testers.
  • Over time, test automation took root. In several cases, test automation is the only feasible approach when you need to run a large number of test cases, fast and with high efficiency.
  • AI-enabled testing is making test automation smarter by using quantities of data. QA engineers can feed historical data into algorithms to increase detection rates, implement automated code reviews, and automatically generate test cases.

Let’s take an overview of what AI can do in Software Testing.

The Potential of AI in Software Testing:

​As organizations aim for continuous delivery and faster software development cycles, AI-led testing will become a more established part of quality assurance. When considering only software testing tasks, there are several tasks that quality Assurance engineers perform multiple times. Automating them can drive huge increases in productivity and efficiency.

In addition to the repetitive tasks, there are also several tasks that are similar in nature, which, if automated, will make the life of a software tester easier. And AI can help identify such fit cases for automation. For instance, the automated UI test cases that fail every time we make a change in a UI element’s name can be fixed by changing the name of an element in the test automation tool.

Artificial Intelligence has several use cases in software testing, including test case execution, test planning, automation of workflows, and maintenance of test cases when there are changes in the code.

But what are the limitations?

Why AI will not take over entire QA phases?

Even though Artificial Intelligence holds strong promise for testing, it will be hard for mere technology to completely take over.

  1. Humans need to oversee AI:
  2. Artificial Intelligence can’t (yet) function on its own without human interference. Until then, organizations need human specialists to create the AI and to oversee operational aspects that are automated with AI. In short manual testers will always be a part of the testing strategy to ensure bug-free software.

  3. AI is not as sophisticated as human logic:While there have been significant advancements in Artificial Intelligence, it does not beat the logic, intuitiveness, and empathy inherent in humans. AI will bring about more impactful change in the way it assists software testers to help them perform their tasks with more accuracy, precision, and efficiency. But for all tasks that need more creativity, intuitive decision making, and user-focused assessments, it may have to be human software testers who hold the fort. For a while at least!
  4. AI can’t, and never will, eliminate the need for humans in Testing:
  5. Organizations can use AI-based testing tools to cover the basics of software testing, and easily uncover defects by auto-generating test cases and executing them for desktop or mobile. However, such an approach isn’t feasible when you need to assess a complex software product with various functions and features to test. Experienced software QA engineers bring a wealth of insights to the table that goes beyond the data. They can make the decisions that must be made even when data doesn’t exist. When a new feature is being implemented, AI may struggle to find enough solid data to define the way forward. Experienced software testers may be better suited to such situations where they can make intuitive leaps based on nothing more than their judgment.

  6. Functions in Software Testing that can’t be entirely trusted to AI:
  7. AI can seamlessly help with tasks that are repetitive in nature and have been done before. But, even if we leverage AI to its full potential, there are jobs within QA that demand human assistance.

    • Documentation Review – Comprehensively learning about the ins and outs of a software system and determining the length and breadth of testing required in it is something better trusted to a human.
    • Creating Tests for Complex Scenarios – Complex test cases that span several features within a software solution may be better done by a QA tester.
    • UX Testing – User experience can be tested and assured only when a user navigates the software or application. How something looks to the users and, more importantly, how it feels to them, is a task beyond the likely capabilities of AI.

Just like automation aims at reducing manual labor by addressing monotonous tasks, AI-led QA minimizes repetitive work with added intelligence by taking it up a notch up.

This means QA engineers should keep doing what they do best. However, it will help QA testers to familiarize themselves with technologies AI to advance their career when these tools become commonplace. The truth is that AI is making a stand, but we still need diligent, creative, and expert QA engineers on our product development teams.

Test Automation Latest Trends

What’s New in Test Automation?

With the arrival of Agile and DevOps development technologies, the software development industry has gone through a significant disruption. Which naturally, has impacted test automation as well. Quality Assurance professionals have had to quickly adapt to the changes in the industry to stay relevant.In some ways, the pace of change is only accelerating. Let’s take a look at some of the latest trends in test automation:

  1. Enhanced Scope of Test Automation:
  2. Test automation was primarily designed to test the application against its expected behavior. However, today, automation teams have to think past the actual scope of test validations to verify a build before its release. Test automation is now used in CI/CD modeling, continuous integration, and delivery, aggressively.

    With the advent of CI-CD and agile development, delivery models with faster time-to-market are coming into vogue. The coverage of test automation has spread across Mobile and Web applications, enterprise systems, and even IoT applications. All automation tools now support a wide variety of application streams.

  3. Increased Pressure to Shorten Delivery Cycles:
  4. The need for test management tools has expanded to facilitate ever-shortening delivery cycles. Companies are investing heavily in improving their development and delivery processes by making use of new and improved tools. Test automation is an integral part of this process.

    Frequent changes in technologies, platforms, and devices have put tremendous pressure on software development teams to deliver solutions faster and more often. By integrating test automation with development, companies can stay on track with market requirements and shorten their delivery cycles.

  5. Integration:
  6. As mentioned earlier, integration plays a pivotal role in shortening delivery cycles. It is also vital when it comes to facilitating test automation intelligently. For smart testing and analytics, the data is consolidated from diverse sources such as requirement management systems, change control systems, task management systems, and test environment.

    The expectation in today’s software development scenario is that the automation suite can execute untended on each code drop regardless of the environment. The need is for it to run through and log failures and successes. In other words, the scope of automation has evolved from test validation to a fully unattended build certification.  Though the code required to verify a scenario is the same, software teams have to evaluate all the ways to integrate it to perform unattended integrations.

  7. Big Data Testing:
  8. Today we live in the day and age of big data. Businesses are going through digital transformation, and data holds critical importance in gaining insights. Essentially, Big Data is large volumes of multiple different kinds of data that is generated at a tremendous velocity. Naturally, this change brings about the need for Big Data testing.

    Test automation in Big Data testing focuses on both performance testing and functional testing. In Big Data testing, it is vital to verify that terabytes of data are favorably processed using commodity cluster and other supportive components. The success of Big Data testing largely depends on the quality of the data.  Hence, the quality of data is validated before test automation begins.

    The data quality is reviewed based on several characteristics such as conformity, accuracy, validity, consistency, duplication, data completeness, etc.

  9. Union of Test Automation and Machine Learning:
  10. Machine learning has brought about some significant changes in workflows and processes. This includes the test automation processes too. In test automation, machine learning can be used to classify redundant and unique test cases; to predict the critical parameters of software testing processes based on historical data; to determine the tests cases which need to be executed automatically; to extract keywords to achieve test coverage; to identify high-risk areas of the application for the prioritization of regression test cases.


As technology gets more advanced, there is tremendous pressure for development iterations to get shorter. By default, this makes quality-related expectations more complex. With massive shifts in the software development field, the test automation process has evolved tremendously, and it will continue to develop in the future.

In a race against time and driven by the need for world-class quality, test automation will remain a strategic investment for businesses to reduce costs while overcoming challenges related to quality and time-to-market. On that journey, of course, only one thing can be predicted with any degree of certainty. And it’s that as software development keeps evolving, testing and test automation will keep evolving as well.

automation testing microservices

Test Automation for Microservices- Here’s What You Need to Know

We have written a couple of times in the past about Microservices. The approaches are evolving, and this blog is an attempt to address a specific question -while testing microservices, does test automation have a role?

Just a little refresher first. As the name suggests, microservices are nothing but a combination of multiple small services that make up a whole. It is a unique method of developing software systems that focus on creating single-function modules with well-defined interfaces and operations. An application built as microservices can be broken down into multiple component services. Each of these services can be deployed, modified, and then redeployed individually without compromising the integrity of an application. This enables you to change one or more distinct services (as and when required) instead of having to redeploy the application as a whole.

Microservices are also highly intelligent. They receive requests, process them, and produce a response accordingly. They have smart points that process information and apply logic, and then direct the flow of the information.

Microservices architecture is ideal in the case of evolutionary systems, for eg. where it is not possible to thoroughly anticipate the types of devices that may be accessing the application in the future. Many software products start based on a monolithic architecture but can be gradually revamped to microservices as and when unforeseen requirements surface that interact over an older unified architecture through APIs.

Why is Testing for Microservices Complicated?

In the traditional approach to testing, every bit of code needs to be tested individually using unit tests. As parts are consolidated together, they should be tested with integration testing. Once all these tests pass, a release candidate is created. This, in turn, is put through system testing, regression testing, and user-acceptance testing. If all is well, QA will sign-off, and the release will roll out. This might be accelerated while developing in Agile, but the underlying principle would hold.

This approach does not work for testing microservices. This is mainly because apps built on microservices use multiple services. All these services may not be available on staging at the same time or in the same form as they are during production. Secondly, microservices scale up and share the demand. Therefore, testing microservices using traditional approaches can be difficult. In that scenario, an effective way to conduct microservices testing is to leverage test automation.

Quick Tips on How to Automate Testing for Microservices:

Here are some quick tips that will help you while testing your microservices-based application using test automation.

  • Manage each service as a software module.
  • List the essential links in your architecture and test them
  • Do not attempt to gather the entire microservices environment in a small test setup.
  • Test across different setups.

How to Conduct Test Automation for Microservices?

  1. Each Service Should Be Tested Individually: Test automation can be a powerful mechanism for testing microservices. It is relatively easy to create a simple test script that regularly calls the service and matches a known set of inputs against a proposed output. This function by itself will free up your testing team’s time and allow them to concentrate on testing that is more complex.
  2. Test the Different Functionalities of your Microservices-based Application: Once the vital functional elements of the microservices-based application have been identified, they should be tested much like you would conduct integration testing in the traditional approach. In this case, the benefits of test automation are obvious. You can quickly generate test scripts that are run each time one of the microservices is updated. By analyzing and comparing the outputs of the new code with the previous one, you can establish if anything has changed or has broken.
  3. Refrain from Testing in a Small Setup: Instead of conducted testing in small local environments, consider leveraging cloud-based testing. This allows you to dynamically allocate resources as your tests need them and freeing them up when your tests have completed.
  4. Test Across Diverse Setups: While testing microservices, use multiple environments to test your code. The reason behind this is to expose your code to even slight variations in parameters like underlying hardware, library versions, etc. that might affect it when you deploy to production.

Microservices architecture is a powerful idea that offers several benefits for designing and implementing enterprise applications. This is why it is being adopted by several leading software development organizations. A few examples of inspirational software teams leveraging microservices include Netflix, Amazon, eBay, etc. If like these software teams, your product development is also adopting microservices then testing would undoubtedly be in focus. As we have seen, testing these applications is a complex task and traditional methods will not do the job. To thoroughly test an application built on this model, it may be essential to adopt test automation. Would you agree?

10 Mobile App Testing Challenges

10 Essential Testing Stages for your Mobile Apps

2016 was truly the ‘year of the mobile’. Mobile apps are maturing, consumer apps becoming smarter, and there is an increasing emphasis on the consumerization of enterprise apps. Slow, poor-performing and bug-riddled apps have no place in today’s smartphone. Clearly, mobile apps need to be tested thoroughly to ensure the features and functionalities of the application perform optimally. Given that almost all industries are leaning towards mobile apps (Gartner predicts that there will be over 268 billion mobile downloads in 2017 that will generate a revenue of USD $77 billion) to make interactions between them and their consumers faster and more seamless, the demand for mobile testing is on the upswing. Mobile app testing is more complex than testing web applications primarily because of the need to be tested on different platforms.

Unlike web application testing where there is a single dominant platform, mobile apps need to developed and then tested on iOS, Android, and sometimes more platforms. Additionally, unlike desktops, mobile apps must deal with several device form factors. Mobile app testing also becomes more complex as factors such as application type, target audience, distribution channels etc. need to be taken into consideration when designing the test plans and test cases.

10 Mobile App Testing Challenges

Let’s look at 10 essential Testing Stages for Mobile Applications:

  1. Type#1: Installation testing:

    Once the application is ready, tests need to conduct installation testing to ensure that the user can smoothly install or uninstall the application. Additionally, they also have to check that the application is updating properly and does not crash when upgrading from an older version to a newer one. Testers also have to ensure that all application data is completely removed when an application is uninstalled.

  2. Type#2: Target Device and OS testing:

    Mobile testers have to ensure that the mobile app functions as designed across a plethora of mobile devices and operating systems. Using real devices and device simulators testers, they can check the basic application functionality and understand the application behavior across the selected devices and form factors. Applications also have to be tested across all major OS versions in the present installed base to ensure that it performs as designed irrespective of the operating system.

  3. Type#3: UI and UX testing:

    UI and UX testing are essential to test the look and feel of the application. This testing has to be done from the users’ perspective to ensure that the application is intuitive, easy to use, and has industry-accepted interfaces. Testing is needed to ensure that language- translation facilities are available, menus and icons display correctly, and that the application items are synchronized with user actions.

  4. Type#4: Functionality Testing:

    Functionality testing tests the functional behavior of the application to ensure that the application is working according to the specified requirements. This involves testing user interactions and transactions to validate if all mandatory fields are working as designed. Testing is also needed to verify that the device is able to multitask and process requirements across platforms and devices when the app is being accessed. Since functional testing is quite comprehensive, testing teams may have to leverage test automation to increase coverage and efficiency for best results.

  5. Type#5: Interrupt testing:

    Users can be interrupted with calls, SMS, MMS, messages, notifications, network outage, device power cycle notification etc. when using an application. Mobile app testers have to perform interruption testing to ensure that the mobile app can capably handle these interruptions by going into a suspended state and then resuming functions once the interruptions are over. Testers can use monkey tools to generate multiple possible interrupts and look out for app crashes, freezes, UI glitches, battery consumption etc. and ensure that the app resumes the current view post the interruptions.

  6. Type#6: Data Network Testing:

    To provide useful functionalities, mobile apps rely on network connectivity. Conducting network simulation tests to simulate cellular networks for bandwidth issues to identify connectivity problems and bottlenecks and then study their impact on application performance fall under the purview of network testing. Testers have to ensure that the mobile app performs optimally with varying network speeds and is able to handle network transitions with ease.

  7. Type#7: Hardware keys Testing:

    Mobile apps are packed with different hardware and sensors that can be used by the app. Gyroscope sensors, proximity sensors, location sensors, touchless sensors, ambient light sensors etc. and hardware features such as camera, storage, microphone, display etc. all can be used within the application itself. Mobile testers thus, have to test the mobile app in different sensor specific and hardware specific environments to enhance application performance.

  8. Type#8: Performance Testing:

    The objective of performance testing is to ensure that the mobile application is performing optimally understated performance requirements. Performance testing involves the testing of load conditions, network coverage support, identification of application and infrastructure bottlenecks, response time, memory leaks, and application performance when only intermittent phases of connectivity are required.

  9. Type#9: Load testing:

    Testers also have to test application performance in light of sudden traffic surges, and ensure that high loads and stress on the application does not cause it to crash. The aim of load testing is to assess the maximum number of simultaneous users the application can support without impacting performance and assess the applications dependability when there is a surge in the number of users.

  10. Type#10:Security testing:

    Security testing involves gathering all the information regarding the application and identifying threats and vulnerability for the application using static and dynamic analysis of mobile source code. Testers have to check and ensure that the applications data and network security functionalities are in line with the given guidelines and that the application is only using permissions that it needs.

Mobile application testing begins with developing a testing strategy and designing of the test plans. The added complexity of devices, OS’ and usage specific conditions adds a special burden on the software testing function to ensure the most usable and best-performing app. How have you gone about testing your mobile apps to achieve this end?

Looking for developing a testing strategy and designing of the test plans for your mobile application

Related Blogs:

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  2. Angularjs Mobile App Development.

  3. Data Privacy Consideration For Creating Web and Mobile Apps.

  4. Role of Big Data In Mobile App Development.

  5. 16 Types of Software Testing Bugs.

  6. Serverless App Testing Challanges.

  7. 34 Software Testing Metrics & KPI’s.

  8. Entry Exit Criteria In Software Testing.

Role of AI In Software Testing

The Role of AI In Software Testing

According to Gartner, by 2020, AI technologies will be pervasive in almost every new product and service and will also be a top investment priority for CIO’s. 2018 really was all about Artificial Intelligence. Tech giants such as Microsoft, Facebook, Google, Amazon and the like spent billions on their AI initiatives. We started noticing the rise of AI as an enterprise technology. It’s now clear how AI brings new intelligence to everything it touches by exploiting the vast sea of data at hand. Influential voices also started talking about the paradigm shift that this technology would bring to the world of software development. Of course, software testing too has not remained immune to the charms of AI.

Role: AI In Software Testing.

But first, Why do we Need AI for Software Testing?

It seems like we have only just firmly established the role of test automation in the software testing landscape and we must start preparing for further disruptions promised by AI! The rise of test automation was driven by development methodologies such as Agile and the need to ship bug and error-free, robust software products into the market faster. From there we have progressed into the era of daily deployments with the rise of DevOps. DevOps is pushing organizations to accelerate the QA cycle even further, to reduce test overheads, and to enable superior governance. Automating test requirement traceability and versioning are also factors that now need careful consideration in this new development environment.

The “surface area” of testing has also increased considerably. As applications interact with one another through API’s leveraging legacy systems, the complexity tends to increase as the code suites keep growing. As the software economy grows and enterprises push towards digital transformation, businesses now demand real-time risk assessment across the different stages of the software delivery cycle.

The use of AI in software testing could emerge as a response to these changing times and environments. AI could help in developing failsafe applications and to enable greater automation in testing to meet these expanded expectations from testing.

How will AI work in Software Testing?

As we move deeper into the age of digital disruption, the traditional ways of developing and delivering software are inadequate to fuel innovation. Delivery timelines are reducing but the technical complexity is rising. With Continuous Testing gradually becoming the norm, organizations are trying to further accelerate the testing process to bridge the chasm between development, testing, and operations in the DevOps environment.

  1. AI helps organizations achieve this pace of accelerated testing and helps them test smarter and not harder. AI has been called, “A field of study that gives computers the ability to learn without being explicitly programmed”. This being the case, organizations can leverage AI to drive automaton by leveraging both supervised and unsupervised methods.
  2. An AI-powered testing platform can easily recognize changed controls promptly. The constant updates in the algorithms will ensure that even the slightest changes can be identified easily.
  3. AI in test automation can be employed for object application categorizations for all user interfaces very effectively. Upon observing the hierarchy of controls, testers can create AI enabled technical maps that look at the graphical user interface (GUI) and easily obtain the labels for different controls.
  4. AI can also be employed effectively to conduct exploratory testing within the testing suite. Risk preferences can be assigned, monitored, and categorized easily with AI. It can help testers in creating the right heat maps to identify bottlenecks in processes and help in increasing test accuracy.
  5. AI can be leveraged effectively to identify behavioral patterns in application testing, defect analysis, non-functional analytics, analysis data from social media, estimation, and efficiency analysis. Machine Learning, a part of AI, algorithms can be employed to test programs and to generate robust test data and deep insights, making the testing process more in-depth and accurate.
  6. AI can also increase the overall test coverage and the depth and the scope of the tests as well. AI algorithms in software testing can be put to work for test suite optimization, enhancing UI testing, traceability, defect analysis, predicting the next test for queuing, determine pass/fail outcomes for complex and subjective tests, rapid impact analysis etc. Since 80% of all tests are repetitive, AI can free up the tester’s time and helps them focus on the more creative side of testing.


Perhaps the ultimate objective of using AI in software testing is to aim for a world where the software will be able to test, diagnose, and self-correct. This could enable quality engineering and could further reduce the testing time from days to mere hours. There are signs that the use of AI in software testing can save time, money, and resources and help the testers focus their attention on doing the one thing that matters – release great software.

demanding technology skills

5 Most In-Demand Technology Skills

This is now a software-defined world. Almost every company today is a technology company. Every product, in some way, is a technology product. As businesses lean more heavily on technology and software, the software development and technology landscape become even more dynamic. Technology is in a constant state of flux, with one shiny new object outshining the one from yesterday. The stakeholders of software development, the testers, developers, designers etc. thus need to constantly re-evaluate their skills. In this environment of constant change, here are, in my opinion, the five most in-demand technology skills to possess today, and why?

  1. R: Owing to the advances in machine learning, the R programming language is having its coming of age moment now. This open source language has been a workhorse for sorting and manipulating large data sets and has shown its versatility in model building, statistical operations, and visualizations.

    R, over the years, has become a foundational tool in expanding AI to unlock large data blocks. As data became more dominant, R has made itself quite comfortable in the data science arena.

    In fact, this language is predicted to surpass the use of Python in data science as R, in contrast to Python, allows robust statistical models to be written in just a few lines. As the world falls more in love with data science it will also find itself getting closer to R.

  2. React: Amongst client-side technologies, React has been growing in popularity rapidly. While the number of frameworks based on JavaScript continues to increase, React still dominates this space. Open Sourced by Facebook in 2013, React has been climbing up the technology charts owing to its ease of use, high level of flexibility and responsiveness, its virtual DOM (document object model) capabilities, its downward data binding capabilities, the ease of enabling migrations, and light-weightiness.

    React is also winning in the NPM download race and has won the crown of the Best JavaScript framework of 2018. In the age of automation, React gives developers a framework that allows them to break down complex components and reuse codes to complete projects faster.

    Its unique syntax that allows HTML quotes, as well as HTML tag syntax, help in promoting construction of machine-readable codes. React also gives developers the flexibility to break down complex UI/UX development into simpler components and allows them to make every component intuitive. It also has excellent runtime performance.

  3. Swift: In 2017 we heard reports of the declining popularity of Swift. One of the main reasons for the same was a perceived preference among developers’ to use multiplatform tools. Swift, that is merely four years old, ranked 16 on the TIOBE index despite having a good start. The reason was mainly the changing methodologies in the mobile development ecosystem.

    However, in 2018 we seem to be witnessing the rise of Swift once again. According to a study conducted by analyst firm RedMonk, Swift tied with Object C at rank 10 in their January 2018 report. It fell one place in the June report, but that could be attributed to the lack of a server-side presence, something IBM has been working to rectify in keeping with its enterprise push.

    Once Swift became open source it has grown in popularity and has also matured as a language. With iOS apps proving to be more profitable than Android apps, we can expect more developers to switch to Swift. Swift is also finding its way into business discussions as enterprises look at robust iOS apps that offer performance as well as security.

  4. Test Automation: Organizations are racing to achieve business agility. This drive has promoted the rise of new development methodologies and the move towards continuous integration and continuous delivery. In this need for speed Test automation will continue to rise in prominence as it enables faster feedback. The push towards digital transformation in enterprises is also putting the focus on testing and quality assurance.

    I expect Shift-left testing to grow to hasten software development. Test automation is rapidly emerging as the enabler of software confidence. With the rising interest in new technologies like IoT and blockchain, test automation is expected to get a further push.

    The possible role of AI in testing is also something to look out for as AI could bring in more intelligence, validation, efficiency, and automation to testing. These could be exciting times for those in the testing and test automation space.

  5. UX: Statistics reveal that 90% of users stop using an application with a bad UX. 86% of users uninstall an app if they encounter problems with its functionality in design. UX or User Experience will continue to rise in prominence as it is the UX that earns users interest and ultimately their loyalty. The business value of UX will rise even further as we delve deeper into the app economy.

    The role of UX designers is becoming even more compelling as we witness the rise of AR, chatbots and virtual assistants. With the software products and services market becoming increasingly competitive, businesses have to focus heavily on UX design to deliver intuitive and coherent experiences to their users that drive usage and foster adoption.

It is an exciting time for us in the technology game. Innovation, flexibility, simplicity, reliability, and speed have become important contributors to software success. The key differentiator in these dynamic times may be the technology skills that you as an individual or as a technology-focused organization possess. To my mind, the skills that will help you stay ahead are those I’ve identified here.

QA interview question answers

Top 90 QA Interview Questions Answers

Let’s dive into Top 90 QA Interview Questions answers that we will recommend you while appearing for any QA interview.

  1. What is Software Quality Assurance (SQA)?
  2. Software quality assurance is an umbrella term, consisting of various planned process and activities to monitor and control the standard of whole software development process so as to ensure quality attribute in the final software product.

  3. What is Software Quality Control (SQC)?
  4. With the purpose similar to software quality assurance, software quality control focuses on the software instead to its development process to achieve and maintain the quality aspect in the software product.

  5. What is Software Testing?
  6. Software testing may be seen as a sub-category of software quality control, which is used to remove defects and flaws present in the software, and subsequently improves and enhances the product quality.

  7. Whether, software quality assurance (sqa), software quality control (sqc) and software testing are similar terms?
  8. No, but the end purpose of all is same i.e. ensuring and maintaining the software quality.

  9. Then, what’s the difference between SQA, SQC and Testing?
  10. SQA is a broader term encompassing both SQC and testing in it and ensures software development process quality and standard and subsequently in the final product also, whereas testing which is used to identify and detect software defects is a sub-set of SQC.

  11. What is software testing life cycle (STLC)?
  12. Software testing life cycle defines and describes the multiple phases which are executed in a sequential order to carry out the testing of a software product. The phases of STLC are requirement, planning, analysis, design, implementation, execution, conclusion and closure.

  13. How STLC is related to or different from SDLC (software development life cycle)?
  14. Both SDLC and STLC depict the phases to be carried out in a subsequent manner, but for different purpose. SDLC defines each and every phase of software development including testing, whereas STLC outlines the phases to be executed during a testing process. It may be inferred that STLC is incorporated in the SDLC phase of testing.

  15. What are the phases involved in the software testing life cycle?
  16. The phases of STLC are requirement, planning, analysis, design, implementation, execution, conclusion and closure.

  17. Why entry criteria and exit criteria is specified and defined?
  18. Entry and exit criteria is defined and specified to initiate and terminate a particular testing process or activity respectively, when certain conditions, factors and requirements is/are being met or fulfilled.

  19. What do you mean by the requirement study and analysis?
  20. Requirement study and analysis is the process of studying and analysing the testable requirements and specifications through the combined efforts of QA team, business analyst, client and stakeholders.

  21. What are the different types of requirements required in software testing?
  22. Software/functional requirements, business requirements and user requirements.

  23. Is it possible to test without requirements?
  24. Yes, testing is an art, which may be carried out without requirements by a tester by making use of his/her intellects possessed, acquired skills and gained experience in the relevant domain.

  25. Differentiate between software requirement specifications (SRS) and business requirement specification (BRS).
  26. SRS layouts the functional and non-functional requirements for the software to be developed whereas BRS reflects the business requirement i.e., the business demand of a software product as stated by the client.

  27. Why there is a bug/defect in software?
  28. A bug or a defect in software occurs due to various reasons and conditions such as misunderstanding or requirements, time restriction, lack of experience, faulty third party tools, dynamic or last time changes, etc.

  29. What is a software testing artifact?
  30. Software testing artifact or testing artifact are the documents or tangible products generated throughout the testing process for the purpose of testing or correspondence amongst the team and with the client.

  31. What are test plan, test suite and test case?
  32. Test plan defines the comprehensive approach to perform testing of the system and not for the single testing process or activity. A test case is based on the specified requirements & specifications define the sequence of activities to verify and validate one or more than one functionality of the system. Test suite is a collection of similar types of test cases.

  33. How to design test cases?
  34. Broadly, there are three different approaches or techniques to design test cases. These are

    • Black box design technique, based on requirements and specifications.
    • White box design technique based on internal structure of the software application.
    • Experience based design technique based on the experience gained by a tester.
  35. What is test environment?
  36. A test environment comprises of necessary software and hardware along with the network configuration and settings to simulate intended environment for the execution of tests on the software.

  37. Why test environment is needed?
  38. Dynamic testing of the software requires specific and controlled environment comprising of hardware, software and multiple factors under which a software is intended to perform its functioning. Thus, test environment provides the platform to test the functionalities of software in the specified environment and conditions.

  39. What is test execution?
  40. Test execution is one of the phases of testing life cycle which concerns with the execution of test cases or test plans on the software product to ensure its quality with respect to specified requirements and specifications.

  41. What are the different levels of testing?
  42. Generally, there are four levels of testing viz. unit testing, integration testing, system testing and acceptance testing.

  43. What is unit testing?
  44. Unit testing involves the testing of each smallest testable unit of the system, independently.

  45. What is the role of developer in unit testing?
  46. As developers are well versed with their lines of code, they are preferred and being assigned the responsibility of writing and executing the unit tests.

  47. What is integration testing?
  48. Integration testing is a testing technique to ensure proper interfacing and interaction among the integrated modules or units after the integration process.

  49. What are stubs and drivers and how these are different to each other?
  50. Stubs and drivers are the replicas of modules which are either not available or have not been created yet and thus they works as the substitutes in the process of integration testing with the difference that stubs are used in top bottom approach and drivers are used in bottom up approach.

  51. What is system testing?
  52. System testing is used to test the completely integrated system as a one system against the specified requirements and specifications.

  53. What is acceptance testing?
  54. Acceptance testing is used to ensure the readiness of a software product with respect to specified requirement and specification in order to get readily accepted by the targeted users.

  55. Different types of acceptance testing.
  56. Broadly, acceptance testing is of two types-alpha testing and beta testing. Further, acceptance testing can also be classified into following forms:

    • Operational acceptance testing
    • Contract acceptance testing
    • Regulation acceptance testing
  57. Difference between alpha and beta testing.
  58. Both alpha and beta testing are the forms of acceptance testing where former is carried out at development site by the QA/testing team and the latter one is executed at client site by the intended users.

  59. What are the different approaches to perform software testing?
  60. Generally, there are two approaches to perform software testing viz. Manual testing and Automation. Manual testing involves the execution of test cases on the software manually by the tester whereas automation process involves the usage of automation framework and tools to automate the task of test scripts execution.

  61. What is the advantage of automation over manual testing approach and vice-versa?
  62. In comparison to manual approach of testing, automation reduces the efforts and time required in executing the large amount of test scripts, repetitively and continuously for a longer period of time with accuracy and precision.

  63. Is there any testing technique that does not needs any sort of requirements or planning?
  64. Yes, but with the help of test strategy using check lists, user scenarios and matrices.

  65. Difference between ad-hoc testing and exploratory testing?
  66. Both ad-hoc testing and exploratory testing are the informal ways of testing the system without having proper planning & strategy. However, in ad-hoc testing, a tester is well-versed with the software and its features and thereby carries out the testing whereas in exploratory, he/she gets to learn and explore more about the software during the course of testing and thus tests the system gradually along with software understanding and learning throughout the testing process.

  67. How monkey testing is different from ad-hoc testing?
  68. Both monkey and ad-hoc testing are the informal approach of testing but in monkey testing, a tester does not requires the pre-understanding and detailing of the software, but learns about the product during the course of testing whereas in ad-hoc testing, tester has the knowledge and understanding of the software.

  69. Why non-functional testing is equally important to functional testing?
  70. Functional testing tests the system’s functionalities and features as specified prior to software development process. It only validates the intended functioning of the software against the specified requirement and specification but the performance of the system to function in the unexpected circumstances and conditions in real world environment at the users end and to meet customer satisfaction is done through non-functional testing technique. Thus, non-functional testing looks after the non-functional traits of the software.

  71. Which is a better testing methodology: black-box testing or white-box testing?
  72. Both black-box and white-box testing approach have their own advantages and disadvantages. Black-box testing approach enables testers to externally test the system on the basis of specified requirement and specification and does not provide the scope of testing the internal structure of the system, whereas white-box testing methodology verify and validates the software quality through testing of its internal structure and working.

  73. If black-box and white-box, then why gray box testing?
  74. Gray box testing is a third type of testing and a hybrid form of black-box and white-box testing approach, which provides the scope of externally testing the system using test plans and test cases derived from the knowledge and understanding of internal structure of the system.

  75. Difference between static and dynamic testing of software.
  76. The primary difference between static and dynamic testing approach is that the former does not involves the execution of code to test the system whereas latter approach requires the code execution to verify and validate the system quality.

  77. Smoke and Sanity testing are used to test software builds. Are they similar??
  78. Although, both smoke and sanity testing is used to test software builds but smoke testing is used to test the initial build which are unstable whereas sanity tests are executed on relatively stable builds which had undergone multiple time through regression testing.

  79. When, what and why to automate?
  80. Automation is preferred when the execution of tests needs to be carried out repetitively for a longer period of time and within the specified deadlines. Further, an analysis of ROI on automation is desired to analyse the cost-benefit model of the automation. Preferably functional, regression and functional tests may be automated. Further, tests which requires accuracy and precision, and is time-consuming may be considered for automation, including data driven tests also.

  81. What are the challenges faced in automation?
  82. Some of the common challenges faced in the automation are

    • Initial cost is very high along with the maintenance costs. Thus, requires proper analysis to assess ROI on automation.
    • Increased complexities.
    • Limited time.
    • Demands skilled tester, having appropriate knowledge of programming.
    • Automation training cost and time.
    • Selection of right and appropriate tools and frameworks.
    • Less flexible.
    • Keeping test plans and cases updated and maintained.
  83. Difference between retesting and regression testing.
  84. Both retesting and regression testing is done after modification in software features and configuration to remove or correct the defect(s). However, retesting is done to validate that the identified defects has been removed or resolved after applying patches while regression testing is done to ensure that the modification in the software doesn’t impacts or affects the existing functionalities and originality of the software.

  85. How to categorize bugs or defects found in the software?
  86. A bug or a defect may be categorized on the priority and severity basis, where priority defines the need to correct or remove defect, from business perspective, whereas severity states the need to resolve or eliminate defect from software requirement and quality perspective.

  87. What is the importance of test data?
  88. Test data is used to drive the testing process, where diverse types of test data as inputs are provided to the system to test the response, behaviour and output of the system, which may be desirable or unexpected.

  89. Why agile testing approach is preferred over traditional way of testing?
  90. Agile testing follows the agile model of development, which requires no or less documentation and provides the scope of considering and implementing the dynamic and changing requirements along with the direct involvement of client or customer to work on their regular feedbacks and requirements to provide software in multiple and short iterative cycles.

  91. What are the parameters to evaluate and assess the performance of the software?
  92. Parameters which are used to evaluate and assess the performance of the software are active defects, authored tests, automated tests, requirement coverage, no. of defects fixed/day, tests passed, rejected defects, severe defects, reviewed requirements, test executed and many more.

  93. How important is the localization and globalization testing of a software application?
  94. Globalization and localization testing ensures the software product features and standards to be globally accepted by the world wide users and to meet the need and requirements of the users belonging to a particular culture, area, region, country or locale, respectively.

  95. What is the difference between verification and validation approach of software testing?
  96. Verification is done throughout the development phase on the software under development whereas validation is performed over final product produced after the development process with respect to specified requirement and specification.

  97. Does test strategy and test plan define the same purpose?
  98. Yes, the end purpose of test strategy and test plan is same i.e. to works as a guide or manual to carry out the software testing process, but still they both differs.

  99. Which is better approach to perform regression testing: manual or automation?
  100. Automation would provide better advantage in comparison to manual for performing regression testing.

  101. What is bug life cycle?
  102. Bug or Defect life cycle describes the whole journey or the life of a defect through various stages or phases, right from when it is identified and till its closure.

  103. What are the different types of experience based testing techniques?
  104. Error guessing, checklist based testing, exploratory testing, attack testing.

  105. Whether a software application can be 100% tested?
  106. No, as one of the principles of software testing states that exhaustive testing is not possible.

  107. Why exploratory testing is preferred and used in the agile methodology?
  108. As agile methodology requires the speedy execution of the processes through small iterative cycles, thereby calls for the quick, and exploratory testing which does not depends on the documentation work and is carried out by tester through gradual understanding of the software, suits best for the agile environment.

  109. Difference between load and stress testing.
  110. The primary purpose of load and stress testing is to test system’s performance, behaviour and response under different varied load. However, stress testing is an extreme or brutal form of load testing where a system under increasing load is subjected to certain unfavourable conditions like cut down in resources, short or limited time period for execution of task and various such things.

  111. What is data driven testing?
  112. As the name specifies, data driven testing is a type of testing, especially used in the automation, where testing is carried out and drive by the defined sets of inputs and their corresponding expected output.

  113. When to start and stop testing?
  114. Basically, on the availability of software build, testing process starts. However, testing may be started early with the development process, as soon as the requirements are gathered and available. Moreover, testing depends upon the requirement of the software development model like in waterfall model, testing is done in the testing phase, whereas in agile testing is carried out in multiple and short iteration cycle.

    Testing is an infinite process as it is impossible to make a software 100% bug free. But still, there are certain conditions specified to stop testing such as:

    • Deadlines
    • Complete execution of the test suites and scripts.
    • Meeting the specified exit criteria for a test.
    • High priority and severity bugs are identified and resolved.
    • Complete testing of the functionalities and features.
  115. Whether exhaustive software testing is possible?
  116. No

  117. What are the merits of using the traceability matrix?
  118. The primary advantage of using the traceability matrix is that it maps the all the specified requirements with that to test cases, thereby ensures complete test coverage.

  119. What is software testability?
  120. Software testability comprises of various artifacts which gives the estimation about the efforts and time required in the execution of a particular testing activity or process.

  121. What is positive and negative testing?
  122. Positive testing is the activity to test the intended and correct functioning of the system on being fed with valid and appropriate input data whereas negative testing evaluates the system’s behaviour and response in the presence of invalid input data.

  123. Brief out different forms of risks involved in software testing.
  124. Different types of risks involved in software testing are budget risk, technical risk, operational risk, scheduled risk and marketing risk.

  125. Why cookie testing?
  126. Cookie is used to store the personal data and information of a user at server location, which is later used for making connections to web pages by the browsers, and thus it is essential to test these cookies.

  127. What constitutes a test case?
  128. A test case consists of several components. Some of them are test suite id, test case id, description, pre-requisites, test procedure, test data, expected results, test environment.

  129. What are the roles and responsibilities of a tester or a QA engineer?
  130. A QA engineer has multiple roles and is bounded to several responsibilities such as defining quality parameters, describing test strategy, executing test, leading the team, reporting the defects or test results.

  131. What is rapid software testing?
  132. Rapid software testing is a unique approach of testing which strikes out the need of any sort of documentation work, and motivates testers to make use of their thinking ability and vision to carry out and drive the testing process.

  133. Difference between error, defect and failure.
  134. In the software engineering, error defines the mistake done by the programmers. Defect reflects the introduction of bugs at production site and results into deviation in results from its expected output due to programming mistakes. Failure shows the system’s inability to execute functionalities due to presence of defect. i.e. defect explored by the user.

  135. Whether security testing and penetration testing are similar terms?
  136. No, but both testing types ensure the security mechanism of the software. However, penetration testing is a form of security testing which is done with the purpose to attack the system to ensure not only the security features but also its defensive mechanism.

  137. Distinguish between priority and severity.
  138. Priority defines the business need to fix or remove identified defect whereas severity is used to describe the impact of a defect on the functioning of a system.

  139. What is test harness?
  140. Test harness is a term used to collectively define various inputs and resources required in executing the tests, especially the automated tests to monitor and assess the behaviour and output of the system under different varied conditions and factors. Thus, test harness may include test data, software, hardware and many such things.

  141. What constitutes a test report?
  142. A test report may comprise of following elements:

    • Objective/purpose
    • Test summary
    • Logged defects
    • Exit criteria
    • Conclusion
    • Resources used
  143. What are the test closure activities?
  144. Test closure activities are carried out the after the successful delivery or release of the software product. This includes collection of various data, information, testwares pertaining to software testing phase so as to determine and assess the impact of testing on the product.

  145. List out various methodologies or techniques used under static testing.
    • Inspection
    • Walkthroughs
    • Technical reviews
    • Informal reviews
  146. Whether test coverage and code coverage are similar terms?
  147. No, code coverage amounts the percentage of code covered during software execution whereas test coverage concerns with the test cases to cover specified functionality and requirement.

  148. List out different approaches and methods to design tests.
  149. Broadly, there are different ways along with their sub techniques to design test cases, as mentioned below

    • Black Box design technique- BVA, Equivalence Partitioning, use case testing.
    • White Box design technique- statement coverage, path coverage, branch coverage
    • Experience based technique- error guessing, exploratory testing
  150. How system testing is different to acceptance testing?
  151. System testing is done with the perspective to test the system against the specified requirements and specification whereas acceptance testing ensures the readiness of the system to meet the needs and expectations of a user.

  152. Distinguish between use case and test case.
  153. Both use case and test case is used in the software testing. Use case depicts and defines the user scenarios including various possible path taken by the system under different conditions and circumstances to execute a particular task and functionality. On the other side, test case is a document based on the software and business requirements and specification to verify and validate the software functioning.

  154. What is the need of content testing?
  155. In the present era, content plays a major role in creating and maintaining the interest of the users. Further, the quality content attracts the audience, makes them convinced or motivated over certain things, and thus is a productive input for the marketing purpose. Thus, content testing is a must testing to make your software content suitable for your targeted users.

  156. List out different types of documentation/documents used in the software testing.
    • Test plan.
    • Test scenario.
    • Test cases.
    • Traceability Matrix.
    • Test Log and Report.
  157. What is test deliverables?
  158. Test deliverables are the end products of a complete software testing process- prior, during and after the testing, which is used to impart testing analysis, details and outcomes to the client.

  159. What is fuzz testing?
  160. Fuzz testing is used to discover coding flaws and security loopholes by subjecting system with the large amount of random data with the intent to break the system.

  161. How testing is different with respect to debugging?
  162. Testing is done with the purpose of identifying and locating the defects by the testing team whereas debugging is done by the developers to fix or correct the defects.

  163. What is the importance of database testing?
  164. Database is an inherited component of a software application as it works as a backend system of the application and stores different types of data and information from multiple sources. Thus, it is crucial to test the database to ensure integrity, validity, accuracy and security of the stored data.

  165. What are the different types of test coverage techniques?
    • Statement Coverage
    • Branch Coverage
    • Decision Coverage
    • Path Coverage
  166. Why and how to prioritize test cases?
  167. Due to abundance of test cases for the execution within the given testing deadline arises the need to prioritize test cases. Test prioritization involves the reduction in the number of test cases, and selecting & prioritizing only those which are based on some specific criteria.

  168. How to write a test case?
  169. Test cases should be effective enough to cover each and every feature and quality aspect of software and able to provide complete test coverage with respect to specified requirements and specifications.

  170. How to measure the software quality?
  171. There are certain specified parameters, namely software quality metrics which is used to assess the software quality. These are product metrics, process metrics and project metrics.

  172. What are the different types of software quality model?
    • Mc Call’s Model
    • Boehm Model
    • FURPS Model
    • IEEE Model
    • SATC’s Model
    • Ghezzi Model
    • Capability Maturity Model
    • Dromey’s quality Model
    • ISO-9126-1 quality model
  173. What different types of testing may be considered and used for testing the web applications?
    • Functionality testing
    • Compatibility testing
    • Usability testing
    • Database testing
    • Performance testing
    • Accessibility testing
  174. What is pair testing?
  175. Pair testing is a type of ad-hoc testing where pair of testers or tester and developer or tester & user is being formed which are responsible for carrying out the testing of the same software product on the same machine.

Hope these 90 QA Questions has provided you a complete overview of the QA process. We wish above QA interview questions will help you clear your next QA interview. Do share your feedback with us @ and let us know how these QA questions have helped you during your QA interview.

Functional test automation: Complete Guide

Ultimate Guide to Functional Test Automation

Testing your newly-designed code for bugs and malfunction is an important part of the development process. After all, your application or piece of code will be used in different systems, environments, and scenarios after shipping.

According to statistics, 36% of developers claim that they will not implement any new coding techniques or technologies in their work at least for the coming year. This goes to show how fast the turnaround times are in the software development world.

It’s often better to ship a slightly less ambitious but functional product than a groundbreaking, unstable one. However, you can achieve both if you automate your quality assurance processes carefully. Let’s take a look at how and why you should automate your functional tests for a quick and valuable feedback during the coding process.

Benefits of Functional Testing & Automation:

  • Maintaining your Reputation:
    Whether you are a part of a large software development company or an independent startup project, your reputation plays a huge role in the public perception of your work. Research shows that 17% of developers agree that unrealistic expectations are the biggest problem in their respective fields. Others state that lack of goal clarity, prioritization, and a lack of estimation also add to the matter.
    There is always a dissonance between managers and developers, which leads to crunch periods and very quick product delivery despite a lack of QA testing. Automated functional testing of your code can help you maintain a professional image by shipping a working product at the end of the development cycle.
  • Controlled Testing Environment:
    One of the best parts of in-house testing is the ability to go above and beyond with how much stress you put on your code.
    For example, you can strain the application or API with as much incoming data and connections as possible without the fear of the server crashing or some other anomaly. While you can never predict how your code will be used in practice, you can assume as many scenarios as possible and test for those specific scenarios.
  • Early Bug Detection:
    Most importantly, functional test automation allows for constant, day-to-day testing of your developed code. You can detect bugs, glitches, and data bottlenecks very quickly in doing so.
    That way, you will detect problems early in the development stage without relying on test group QA which will or will not come across practical issues. The bugs you discover early on can sometimes steer your development process in an entirely different direction, one that you would be oblivious to without automated, repeated testing.
  1. Is Your Test’s Automation Necessary?
    Before you decide to design your automated functionality test, it’s important to gauge its necessity in the overall scheme of things. Do you really need an automated test at this moment or can you test your code’s functionality manually for the time being?
    The reason behind this question is simple – the use of too much automated testing can have adverse effects on the data you collect from it. More importantly, test design takes time and careful scripting, both of which are valuable in the project’s development process. Make sure that you are absolutely sure that you need automated tests at this very moment before you step into the scripting process.
  2. Separate Testing from Checking:
    Testing and checking are two different things, both of which correlate with what we said previously. In short, when you “check” your code, you will be fully aware, engaged, and present for the process. Testing, on the other hand, is automated and you will only see the end-results as the final data rolls in.
    Both testing and checking are important in the QA of your project, but they can in no way replace one another. Make sure that both are implemented in equal measure and that you double-check everything that seems off or too good to be true manually.
  3. Map out the Script Fully:
    Running a partial script through your code won’t bring any tangible results to the table. Worse yet, it will confuse your developers and lead to even more crunch time. Instead, make sure that your script is fully written and mapped out before you put it into automated testing.
    Make sure that the functional test covers each aspect of your code instead of opting for selective testing. This will ensure that the code is tested for any conflicts and compatibility issues instead of running a step-by-step test.
  4. Multiple Tests with Slight Variations:
    What you can do instead of opting for several smaller tests is to introduce variations into your functionality test script. Include several variations in terms of scenarios and triggers which your code will go through in each testing phase.
    This will help you determine which aspects of your project need more polish and which ones are good as they are. Repeated tests with very small variations in between are a great way to vent out any dormant or latent bugs which can rear their head later on. Avoid unnecessary post-launch bug fixes and last-minute changes by introducing a multi-version functionality test early on.
  5. Go for Fast Turnaround:
    While it is important to check off every aspect of your code in the functional testing phase, it is also important to do so in a timely manner. Don’t rely on overly-complex or long tests in your development process.
    Even with automation and high-quality data to work with afterward, you will still be left with a lot of analysis and rework to be done as a result. Design your scripts so that they trigger every important element in your code without going into full top-to-bottom testing each time you do so. That way, you will have a fast and reliable QA system available for everyday coding – think of it as your go-to spellcheck option as you write your essay.
  6. Identify & Patch Bottlenecks:
    Lastly, it’s important to patch out the bottlenecks, bugs, and glitches you receive via the functional test you automated. Once these problems are ironed out, make sure to run your scripts again and check if you were right in your assertion.
    Running the script repeatedly without any fixes in between runs won’t yield any productive data. As a result, the entire process of functional test automation falls flat due to its inability to course-correct your development autonomously.

In Summation

Once you learn what mistakes are bound to happen again and again, you will also learn to fix them preemptively by yourself without the automated testing script. Use the automation feature as a helpful tool, not as a means to fix your code (which it won’t do by itself).

Patch out your glitches before moving forward and closer to the official launch or delivery of your code to the client. The higher the quality of work you deliver, the better you will be perceived as a professional development firm. It’s also worth noting that you will learn a lot as a coder and developer with each bug that comes your way.

Author: Elisa Abbott is a freelancer whose passion lies in creative writing. She completed a degree in Computer Science and writes about ways to apply machine learning to deal with complex issues. Insights on education, helpful tools, and valuable university experiences – she has got you covered;) When she’s not engaged in assessing translation services for PickWriters you’ll usually find her sipping a cappuccino with a book.