It’s reasonable to assume that DevOps has two parallel, and equally important, objectives. One primary aim of DevOps is to reduce the development lifecycle with continuous delivery of software to the clients and end-users and the other crucial objective is to improve the software quality.
It’s never been up for debate that testing is an extremely critical phase in software development. Now, with transformations in the development cycles with fast-paced approaches, such as DevOps and Agile, how we look at testing has evolved. It is now essential to implement smart ways of testing software products and applications. Test automation is one of the approaches to improve testing speed and accuracy.
DevOps Testing Strategy
Before moving to the test automation mechanisms in the world of DevOps, it is necessary to make a pitstop to examine the factors feeding into the DevOps testing strategy. DevOps supports and includes a continuous testing strategy which means testing is conducted at every phase of the process. Testers are involved in the testing of the development plan, design testing, and operations testing with functional and non-functional testing. For example, risk-based or exploratory testing can be executed to test the software designs. When it comes to releasing, the combination of tests can run on the production and test environments.
The primary idea in the DevOps testing strategy is to continuously look for possible gaps and errors. DevOps involves testing right from the initiation till the very end.
Test Automation and DevOps
As stated earlier, DevOps supports and follows a continuous testing strategy. Also, continuous development and delivery are involved in DevOps. At that pace, a high level of collaboration and fast-paced execution is required to meet the expected efficiency and quality levels.
This is where test automation becomes the key to support the DevOps practices and make sure software quality is always maintained and improved. Some of the best practices for beginning test automation include:
Begin with the test automation flows that are easy and increase the complexity and coverage over time.
Develop independent and self-contained automation test cases.
Maintain collective ownership during test automation.
Collaborate with design, development, and deployment teams.
While following the practices illustrated above, the Test Automation Engineers may still get confused about the mechanisms to integrate these with DevOps. A common workflow is presented below to enable test automation teams to amalgamate automation testing in DevOps practices.
The test engineers shall meet with the developers to discuss the user story and list down the behaviors from a business standpoint. The behaviors identified shall then be converted to Behavior-driven development (BDD) tests.
Developers shall work on the user story and create unit and integration tests in collaboration with the testing team under test-driven development (TDD). The shared code repository shall be set-up and the tests and codes must be deployed in the repository.
DevOps Engineers shall create Continuous Integration (CI) servers to execute the code in the shared repository and execute all the tests in TDD and BDD.
Automation Engineers shall analyze these workflows and tests to create the automated test scripts. The engineers shall also develop additional tests around performance, security, and non-functional testing.
DevOps Engineers shall reuse the test scrips loaded in the shared repository for acceptance testing.
DevOps’ continuous testing strategy involves several resources and the Automation Test Engineers must collaborate with these resources to effectively conduct test automation.
DevOps Test Automation Tools
Obviously, there are many test automation tools available in the market, and making the right choice is complex. To conduct test automation in DevOps, the tool selected must have the following features:
Seamless integration in the CI/CD pipeline.
Platform-independence to run in any of the infrastructures.
Multi-user access to be used by testers, developers, and others at the same time.
The short learning curve for better release management.
Maintenance of automation tests and scripts.
Each tool will come with a set of features and benefits that will decide its aptness for each specific situation. For instance, TestComplete is a typical automation tool that can meet some test automation requirements in DevOps. This is an automated UI testing tool that can support a variety of test cases with enhanced test coverage. The tool comes with record and replay capabilities and an AI-equipped customizable object repository. Tools like these can allow automation test engineers to develop end-to-end tests quickly and efficiently. Good test automation tools can be easily integrated with the various continuous integration systems. Given the prevailing environment with remote teams, it’s also useful to look if the tool comes with distributed testing capabilities. The right set of features will help enhance the testing abilities of the team and also simplify the maintenance tasks.
The bar for software quality has been raised very high and the consequences for failing this test can be dire for a product or application. In the uber-accelerated world of DevOps, software testing has to take on a completely new dimension. Given the need to test more, test faster, and test better, automation presents itself as the most appropriate strategy to achieve software quality.
What is React Testing Library?
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.
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.
The Need For React Testing Library:
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.
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:
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.
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.
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.
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:
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.
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.
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.
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.
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.
Humans need to oversee AI:
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.
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!
AI can’t, and never will, eliminate the need for humans in Testing:
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.
Functions in Software Testing that can’t be entirely trusted to AI:
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.
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:
Enhanced Scope of Test Automation:
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.
Increased Pressure to Shorten Delivery Cycles:
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.
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.
Big Data Testing:
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.
Union of Test Automation and Machine Learning:
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.
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?
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.
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.
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.
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?
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.
In this blog post, we look at ten essential testing stages for mobile applications:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
Let’s dive into Top 90 QA Interview Questions answers that we will recommend you while appearing for any QA interview.
What is Software Quality Assurance (SQA)?
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.
What is Software Quality Control (SQC)?
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.
What is Software Testing?
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.
Whether, software quality assurance (sqa), software quality control (sqc) and software testing are similar terms?
No, but the end purpose of all is same i.e. ensuring and maintaining the software quality.
Then, what’s the difference between SQA, SQC and Testing?
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.
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.
How STLC is related to or different from SDLC (software development life cycle)?
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.
What are the phases involved in the software testing life cycle?
The phases of STLC are requirement, planning, analysis, design, implementation, execution, conclusion and closure.
Why entry criteria and exit criteria is specified and defined?
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.
What do you mean by the requirement study and analysis?
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.
What are the different types of requirements required in software testing?
Software/functional requirements, business requirements and user requirements.
Is it possible to test without requirements?
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.
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.
Why there is a bug/defect in software?
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.
What is a software testing artifact?
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.
What are test plan, test suite and test case?
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.
How to design test cases?
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.
What is test environment?
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.
Why test environment is needed?
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.
What is test execution?
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.
What are the different levels of testing?
Generally, there are four levels of testing viz. unit testing, integration testing, system testing and acceptance testing.
What is unit testing?
Unit testing involves the testing of each smallest testable unit of the system, independently.
What is the role of developer in unit testing?
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.
What is integration testing?
Integration testing is a testing technique to ensure proper interfacing and interaction among the integrated modules or units after the integration process.
What are stubs and drivers and how these are different to each other?
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.
What is system testing?
System testing is used to test the completely integrated system as a one system against the specified requirements and specifications.
What is acceptance testing?
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.
Different types of acceptance testing.
Broadly, acceptance testing is of two types-alpha testing and beta testing. Further, acceptance testing can also be classified into following forms:
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.
What are the different approaches to perform software testing?
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.
What is the advantage of automation over manual testing approach and vice-versa?
Is there any testing technique that does not needs any sort of requirements or planning?
Yes, but with the help of test strategy using check lists, user scenarios and matrices.
Difference between ad-hoc testing and exploratory testing?
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.
How monkey testing is different from ad-hoc testing?
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.
Why non-functional testing is equally important to functional testing?
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.
Which is a better testing methodology: black-box testing or white-box testing?
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.
If black-box and white-box, then why gray box testing?
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.
Difference between static and dynamic testing of software.
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.
Smoke and Sanity testing are used to test software builds. Are they similar??
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.
When, what and why to automate?
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.
What are the challenges faced in automation?
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.
Demands skilled tester, having appropriate knowledge of programming.
Automation training cost and time.
Selection of right and appropriate tools and frameworks.
Keeping test plans and cases updated and maintained.
Difference between retesting and regression testing.
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.
How to categorize bugs or defects found in the software?
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.
What is the importance of test data?
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.
Why agile testing approach is preferred over traditional way of testing?
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.
What are the parameters to evaluate and assess the performance of the software?
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.
How important is the localization and globalization testing of a software application?
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.
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.
Does test strategy and test plan define the same purpose?
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.
Which is better approach to perform regression testing: manual or automation?
Automation would provide better advantage in comparison to manual for performing regression testing.
What is bug life cycle?
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.
What are the different types of experience based testing techniques?
Error guessing, checklist based testing, exploratory testing, attack testing.
Whether a software application can be 100% tested?
No, as one of the principles of software testing states that exhaustive testing is not possible.
Why exploratory testing is preferred and used in the agile methodology?
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.
Difference between load and stress testing.
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.
What is data driven testing?
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.
When to start and stop testing?
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:
Complete execution of the test suites and scripts.
Complete testing of the functionalities and features.
Whether exhaustive software testing is possible?
What are the merits of using the traceability matrix?
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.
What is software testability?
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.
What is positive and negative testing?
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.
Brief out different forms of risks involved in software testing.
Different types of risks involved in software testing are budget risk, technical risk, operational risk, scheduled risk and marketing risk.
Why cookie testing?
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.
What constitutes a test case?
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.
What are the roles and responsibilities of a tester or a QA engineer?
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.
What is rapid software testing?
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.
Difference between error, defect and failure.
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.
Whether security testing and penetration testing are similar terms?
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.
Distinguish between priority and severity.
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.
What is test harness?
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.
What constitutes a test report?
A test report may comprise of following elements:
What are the test closure activities?
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.
List out various methodologies or techniques used under static testing.
Whether test coverage and code coverage are similar terms?
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.
List out different approaches and methods to design tests.
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
How system testing is different to acceptance testing?
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.
Distinguish between use case and test case.
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.
What is the need of content testing?
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.
List out different types of documentation/documents used in the software testing.
Test Log and Report.
What is test deliverables?
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.
What is fuzz testing?
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.
How testing is different with respect to debugging?
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.
What is the importance of database testing?
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.
What are the different types of test coverage techniques?
Why and how to prioritize test cases?
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.
How to write a test case?
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.
How to measure the software quality?
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.
What are the different types of software quality model?
Mc Call’s Model
Capability Maturity Model
Dromey’s quality Model
ISO-9126-1 quality model
What different types of testing may be considered and used for testing the web applications?
What is pair testing?
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 @ [email protected] and let us know how these QA questions have helped you during your QA interview.
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.
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.
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.
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.
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.
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.
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.
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.
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 some critical software testing metrics as well as key performance indicators based on their experience and knowledge.
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:
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.
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
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
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)
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.
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
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
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
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)
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
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
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
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 Percentage: Measures the percentage of critical defects in the software.
(Critical defects / Total defects reported) x 100
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.
(Total time taken for bug fixes / Number of bugs)
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)
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
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
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.
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
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.
Let us now begin our today’s discussion on how to perform usability testing for your website and discuss various methods to do so.
When you visit a website, like Amazon, eBay, etc., what is the one thing that makes you stay there? Is it the design, offers, or the fact that you can use it easily and find relevant information or product effortlessly? Though all these factors are crucial for retaining a visitor, it is the ease of usability and satisfied user experience that guarantees your happiness and encourages you to stay on a website longer.
So, what is this usability and why is it so critical for your websites?
Nowadays, when the number of the competitors is increasing rapidly, design and content are not enough to retain users, it also requires engaging, intuitive, and responsive user experience, which should be considered by the designers and development teams during the development phase.
Usability, which is defined by ISO as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in the specified context of use” is, therefore, an integral part of a website and is ensured with the assistance of usability testing.
The question then arises:
What is usability testing and how it helps ensure the usability of a website?
Asking people to review your work, might be a time-consuming task, but it always works in your favor. This process can be applied to any discipline, especially to improve the user experience.
Usability testing is one such method of user research or review, which is used to validate the design decisions for an interface as well as to verify its quality, accessibility, and usability by testing it with representative users. It helps create a website/product that connects with users and establishes credibility, builds trust, and ensures customer satisfaction.
Usually conducted by the UX Designer or user researcher during each iteration of the product, it enables them to uncover various issues with the website’s user experience and resolve them to ensure it is usable enough.
Hence, usability testing ensures that the interface of a website is built in a way that it accurately fits the user’s expectations and requirements. Moreover, it determines whether it is user-friendly and if users will come back to it or not.
Methods used to test your website:
An area of expertise of UX/UI designers and developers, usability testing, is performed with the assistance of various methods, which help the team accumulate necessary details about the website’s usability.
Popular testing methods are:
A/B testing or split testing is used for an experimental analysis, wherein two versions are compared by the team to choose the best version of the website or its component and to determine the one that performs best.
It uses a qualitative and quantitative analysis that validates the intended goal.
Remote Usability Testing:
Another important method of usability or user testing, remote usability testing is used when the user and researcher are in different geographical locations. This test is moderated by an evaluator interacting with the participants using various screen sharing tools.
It offers developers more realistic insight than lab research and allows them to conduct more research in the shorter period of time.
In co-discovering learning, users are grouped together to test the product, while being observed. Test users talk naturally with one another and are encouraged to define what they are thinking about while performing the allocated task.
This helps measure the time taken to complete different tasks as well as the instances where the users asked for assistance, among other things.
Expert reviews involve UX experts who review the product for any potential issues or defects, which are evaluated by them with the assistance of the following techniques:
This method of usability testing is used to capture physiological data of users conscious and unconscious experiences of using the website. During this testing, the motion of the eye, its movement and position are tracked, to analyze user interactions and time between clicks.
It helps to identify the most eye-catching, confusing and ignored features on the website.
Apart from these testing methods, there are other effective methods that do not require any test lab and can be executed without investing any technical efforts.
Questionnaires, Surveys, & Interviews:
An effective method of usability testing, questionnaires, surveys, & interviews involves asking several questions from the users, which helps the researches get informative feedbacks in real time.
Performed when there is a requirement for a large number of opinions, these methods help avoid ambiguity and deliver structured information.
Realistic Scripts & Scenarios:
This method of usability testing involves both developers and tester, who work together on a preplanned test scenario and imitate the steps that a user while accessing the website.
They act as a user and replicate the anticipated steps a user takes, which are then assessed by the developers to improve the website’s usability.
Drawing on Paper:
Drawing on paper is a popular & cost effective method of usability testing used by designers and developers, wherein they create a website prototypes on a paper and let users test it and its various components, like controls, bars, sliders, etc.
This is an effective testing technique as it allows the developers to gain relevant feedback on the paper prototypes easily.
Think Aloud Protocol:
Also known as lab usability testing, think aloud protocol, is a qualitative data collection technique, used to understand the user’s own reasons for their website usability behavior.
During this process, test sessions are either audio or video recorded for developers future reference.
Whether a website or an app, these methods of usability evaluators can be used by the team to get real users data, which can be utilized to make the product suitable for the target audience.
Now, let’s move on to understanding the process of usability testing.
Process of Website Usability Testing:
The process of usability testing is a simple one and can be executed either by the developers, testers or appointed users. It follows a set of five steps which are:
The test begins with the team identifying the goals and defining the scope of testing. Furthermore, they agree on the metrics, determine the cost of the usability study and create the test plan and test strategy.
Once the necessary plan is prepared, the team and the resources are assembled and the tasks are assigned accordingly. Finally, the team lead or manager decides the reporting tools and templates, which will be used for test execution.
It is in this stage of the process that the team performs the usability test, during which they communicate the scope of testing and capture unbiased results.
After test execution, the team categorizes the results and identifies the patterns among them, which are then used to generate inferences.
Finally, once the analysis of the results is completed, the team offers actionable recommendations as well as stakeholder briefing, to help rectify issues and to remove any issues regarding the testing.
Advantages Offered by Usability Testing:
By investing in usability testing, you will not only make your users and potential clients happy but also reap various other benefits, which might help you increase your ROI and create a renowned reputation in the market.
We’re not through yet:
You will also enjoy various other benefits, like:
Improve Retention Rate:
Retaining customers is an important source of income for the organization in the retail world. By conducting usability testing organizations can improve the retention rate, as it allows them to understand why users are leaving their site and take necessary preventive measures.
It is comparatively cheaper to conduct usability testing, rather than creating a new website or redesigning a one that does not meet the requirements of the user and offers them an unsatisfactory user experience.
Understand User Behavior:
From determining the most engaging elements on the website to identifying the pattern of user behavior, usability testing helps the team immensely and offers them data which can be used to create a better website.
Detect Bugs & Defects:
Usability testing is immensely helpful in detecting defects and bugs that were not visible to the developers.
Reduce Support Calls:
By conducting usability testing, the team can minimize the number of support calls or inquiries users will have to make to the help desk, as they’ll come across fewer usability problems and queries.
So, these are the various ways to perform usability testing for your website.
Now I’d like to turn it over to you:
Which of these methods do you like the most and which one do you find to be the most effective and useful?
Also, if you have any suggestions, let me know in the comments section below.
Digital Transformation – These two words have changed the enterprise as we know it. Given the intense focus on digital, it has become evidently clear that the world will soon be divided into two parts – that of ‘digital leaders’ and of ‘digital laggards’ as per a Harvard Business Report. Unsurprisingly, HBR believes that it is the digital leaders who will outperform the digital laggards. Digital transformation has impacted business models, customer experiences, and operating models. This trend is all about employing digital technologies to business workflows and operations along with customer interactions. The aim is to enhance existing processes and improve the existing modes of interactions and consequently enable new, better, and more relevant products and processes. So pervasive has been the impact of Digital Transformation that it has topped the CIO agenda in 2017 as per a Wall Street Journal report. Having said that, here’s a look at what this widespread adoption of Digital Transformation means for companies like ours who support the organizations that have embarked on this journey.
Web App Development:
The enterprise today has to keep up with an insatiable demand for apps. It is because of the demand for enterprise-grade, secure, robust, and intuitive applications that organizations developing these apps have had to rethink how applications are created. Development methodologies such as Agile, DevOps, Behaviour Driven Development, and Test Driven Development thus have emerged as key enablers of digital transformation. They give organizations the capability to deliver reliable applications faster. Low-code, rapid application development platforms, also, have been thrust into the spotlight to fuel this digital economy that depends on applications. Given that organizations have to be more consumer-focused in this digital age also means an increased focus on UX. Organizations also have to realize that apps now have to be tightly integrated with existing systems and deliver value to the business. The need for IT agility also means that apps become more customized, simple and modular, and highly secure. App development needs to accommodate these needs. As digital transformation becomes stronger, app development also has to factor in the interfaces with and the working of all networking elements, servers, and databases. Insights into how they are likely to perform under application conditions will become key inputs to delivering service assurance. That is our challenge now.
Mobile App Development:
The mobile has a decisive role in digital transformation. The growing mobile obsession irrespective of geographical, cultural, and social diversity means that enterprises have to calibrate their digital transformation initiatives around mobile consumerism. For software partners like is, this means mobile app development has to look at emerging technologies such as bot frameworks, machine learning, AI etc. to elevate mobile apps to match consumer expectations and have a transformational business impact. Having a mobile plan for all the disparate systems, and ensuring all legacy applications have a mobile front-end will be imperative. Mobile app developers also have to take into consideration business intelligence and analytics as more enterprises move towards SaaS applications and the cloud. At the same time, traditional mobile apps will make way for intelligent mobile apps that employ cognitive API’s and focus on delivering hyper-personalized UX’s to finely-tuned mobile app experiences. With greater digital proliferation, mobile app development will also move towards amalgamating experiences of the web with the mobile to develop apps that are extendable, performance oriented, highly secure, discoverable, and shareable.
The shift towards methodologies such as Agile and DevOps is changing the way software is tested. The need for fool-proof, secure, available, comprehensive, and robust applications has never been greater than today. Owing to this, shift left testing is becoming popular. Here testing is integrated into the development process itself and starts early in the development cycle. Testing in the digital world is not only about finding faults but also about assisting in creating an application that focuses on customer experience. Testing teams have to now not only look at the business aspect but also focus on providing intelligence for business creation. The speed of testing has to increase and thus, we have to implement higher levels of test automation and leverage technologies such as AI and Machine Learning to make testing smarter. Software testing teams also have to focus on ensuring consistent application performance across different platforms, mobile devices, and operating systems, even with an increased focus on UX. Most importantly, test automation initiatives have to be open to evolution in keeping with constantly evolving application demands.
The cloud is a key enabler of digital transformation efforts as it offers enterprises the ease, speed, and scale that businesses need. The digital economy demands application availability. There is no place for latency in this business environment. The cloud emerges as the enabler of efficiencies here to ensure the anytime, anywhere availability of applications and information access. The need for greater computing power, storage, and a robust IT infrastructure can be addressed with the cloud. We have to consider that the cloud will become even more pervasive in enterprises looking at the digital transformation. This is inevitable as it provides enterprises with the capability to continuously innovate, build, test, implement, and experiment with different applications on multiple platforms. Additionally, since digital transformation demands the adoption of a culture of collaboration, it enables people to work more efficiently, to find ways to service customers better, generate revenue, and to find solutions to unsolvable problems. The cloud emerges as its critical enabler of innovation, creativity, and productivity and it has to form a key part of our arsenal.
The true value of digital transformation lies in complete transformation- not just tweaks. This transformation implies disruption and halting a previous trajectory to allow a fundamental change of path. It is only then that you can achieve the goal of digital transformation – to raise the bar and change the ground rules so that you can win in this competitive global economy. And yes, it will be software service partners that will help power that transformation.
Mutation testing is one of the newly developed approaches to test a software application by deriving and using the better quality of test cases. The purpose of mutation testing is to evaluate the effectiveness of the test cases to detect errors in the event of modification or changes in the program code. However, these changes are very small so that it does not affect the overall quality of the application program.
The changes introduced or injected in the program code are generally referred as ‘mutants’. These mutants are injected in the lines of code to replace some variables or operands or syntax or conditions or expressions or statements in order to introduce faults in the code.
Let see a simple example to understand the concept of mutant injection in the program code:
1-Read annual salary.
2-If annual salary > Rs.2.50 Lacs.
3-Income Tax = 10% of 2.50 lacs.
Above given are the lines of code which is very easy to understand, thus not explaining them. Now, in the above-given program, we try to inject mutant. Let’s see some of them
1-Read annual salary.
2-If annual salary< Rs.2.50 Lacs.
3-Income Tax = 10% of Rs.2.50 Lacs.
The original program has been changed to the mutant program by replacing the operator ’>’ with the mutant ‘<’. Further, more unique mutants can be injected to create more mutant programs. Let’s see how
1-Read annual salary.
2-If annual salary && Rs.2.50 Lacs.
3-Income Tax = 10% of Rs.2.50 Lacs.
Note:-Invalid operator(&&) injected.
1-Read annual salary.
2-If annual salary > Rs.2.50 Lacs.
Note:-Deleting the line of code/statement.
1-Read annual salary.
2-If annual salary > 2.75 lacs.
3-Income Tax =10% of Rs. 2.50 lacs.
Note:-Changing the value in the statement/line of code.
Now, How to do mutation testing?
We have one original program and its four mutant programs. Test cases with relevant sets of test data are executed over the original and mutant program.
If the results of these test cases are same, then it may be inferred that the test cases are well enough to detect the difference between original and mutant program, and thereby killing the mutant.
And if the results are not same, then it may be concluded that test cases lack to distinguish between original and mutant program, and mutant is still alive. Thus, test cases need to be improved to kill mutants.
Consider following test data for executing test cases over the original program and mutant program-4:
On feeding 2.80, both original and mutant program generate similar results, thus mutant is killed by the test case. However, with the test data value of 2.60, results will be same, thus mutant is alive and is not detected by the test case, thereby needs improvement in the test cases.
Similarly, executing the above-given test data over the original program and mutant program-2, which generates similar results under both test data values i.e. failure. This means that the test cases are quite effective to detect changes and kill mutants.
The above-stated process needs to be repeated for each different mutant program and for each different set of test data to evaluate and improve the effectiveness and quality of the test cases.
Although mutation testing is a time-consuming process but is effective to detect loopholes and flaws in the programming code. However, instead of seeing it as a testing technique, mutation testing may be more seen as a test improvement methodology, which improves the effectiveness and quality of the test cases, which ensures good test coverage and subsequently the better test results.