What the widespread adoption of digital transformation means for us?

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.

  1. 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.

  2. 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.

  3. Software Testing:

    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.

  4. Cloud

    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 – Learn This Interesting Testing Technique Quickly with a Simple Example

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:

Original Program:

1-Read annual salary.

2-If annual salary > Rs.2.50 Lacs.

3-Income Tax = 10% of 2.50 lacs.

4- Endif.

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

Mutant Program-1:

1-Read annual salary.

2-If annual salary< Rs.2.50 Lacs.

3-Income Tax = 10% of Rs.2.50 Lacs.

4-Endif.

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

Mutant Program-2:

1-Read annual salary.

2-If annual salary && Rs.2.50 Lacs.

3-Income Tax = 10% of Rs.2.50 Lacs.

4-Endif.

Note:- Invalid operator(&&) injected.

 

Mutant Program-3:

1-Read annual salary.

2-If annual salary > Rs.2.50 Lacs.

3-

4-End If.

Note:- Deleting the line of code/statement.

 

Mutant Program-4:

1-Read annual salary.

2-If annual salary > 2.75 lacs.

3-Income Tax = 10% of Rs. 2.50 lacs.

4-Endif.

 

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:

  • 2.80.
  • 2.60.

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.

Conclusion:

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.

Are great products due to great developers or great testers?

As the world becomes increasingly software-defined and all products become software products, the focus shifts to not only developing newer, better products but to develop them faster. Along with faster development, there has been a shift in the way quality is perceived today. Can we even imagine using a product that is slow or prone to bugs today? In a software-defined world, quality includes reliability and an assurance of uncompromising security. Software development too has undergone a quantum leap over the last few years. Developers are now the superheroes of this software dominated world developing products using new technologies to make our lives simpler and agiler. Developers don’t just create code but are deeply invested in creating products that generate value in our lives. Given this tectonic shift in the manner in which products are developed one big question that may crop up is, “Are great software products created due to great developers or great testers?”

are great products due to great developers or great testers

First, a caveat. Clearly, product development calls for a bunch of collaborative efforts. Just as vital as development and testing are defining the user’s needs and adoption behavior, designing a great user experience, and obviously impactful marketing and sales. For the purposes of this blog though we will focus on the nuts and bolts of building the product.

To begin that conversation, we have to take a look at the change that has come about in the software development landscape. The need for great software products to be delivered in the shortest timeframe possible has led to the adoption of development methodologies such as Agile and DevOps. These methodologies are all about faster processes, the use of the latest and the most relevant technology options, and a clear alignment with business demands. As software eats the world, businesses have to release software products faster to meet the ever-changing and increasing consumer demands. The success of an organization has become directly proportional to its capability to release, update, and improve its software. Development teams have thus had to become focused on perfecting releases. Key is making incremental changes to the software as the need arises.

The connection of the end-user with the quality of code is also becoming ever-tighter as the consumer base becomes more used to great digital experiences. Developers are now expected to create intelligent apps that include the latest technologies such as virtual personal assistants (VPAs) etc. New technologies have the potential to transform workplaces and make everyday tasks simpler. Clearly, the developers of today have to know exactly what their audience needs from them and how the application is expected to fulfill a business demand. At the same time, they have to create code that rocks the user’s world. Software products are becoming easier to use but harder to build! Developers now have to focus on creating code that has interconnected parts which render themselves to iterations with ease. Without a doubt, developers have to constantly keep an eye out for the latest technological and business trends and remain updated to create stellar products that can survive in today’s intensely competitive marketplace.

While the role of the developer has risen to one of paramount importance and software delivery reaches Formula 1 speed, the role of the tester has evolved as well. In order to finish first in the race for quality software delivery, the focus on software testing has moved from a good-to-have to a must-have. Software testing can no longer remain an end-of-development exercise. As DevOps and Continuous Delivery move from being a competitive advantage to just par for the course, testing becomes more integrated into the development process itself. Can we imagine fast deployment without adequate testing? Can we release quality software products, releases, or updates fast if the speed of testing does not meet the speed of development? Can we, any longer, afford to leave software testing to the end of the development lifecycle?

While developers have been the key people to recast our society with software, it is the testers who decide the strength of the software in production. It is the testing teams that will identify numerous and creative ways to dispassionately break down a software product so that the product, in the hands of the end-user, behaves as it should. Testing teams are utilizing test automation and technologies such as AI to make the testing process smoother, more expansive, and yet faster, to make sure that broken code does not impede product performance or render the product to vulnerabilities. Testers are the superstars who will dare to raise the uncomfortable questions that ultimately elevate the barometer of quality.

If we look at these two roles closely, we can identify that both developers and testers are working with the same intent – that of creating quality products. However, with new development methodologies such as DevOps coming into play, these two roles are becoming inextricably entwined. Development and testing can no longer function in isolation. If you need a great development team, you need an equally strong testing and test automation team to make sure that the final product is accepted in the market.

The way the world is heading, it is clear that great products can only be created when you not only have great developers but great testers as well. Developers and testers thus both become superheroes fighting the quality war in the software universe…one the Guardians of the Galaxy, and the other the Avengers. Despite their differences, they remain superheroes in their own right, and the biggest battles are won only when they fight on the same side!

Be fussy about fuzz testing to avoid software fails

While the cost and effort that goes into software testing are high, what is enormously higher is the cost of a software failure. Did you know? Tricentis’ 2018 Software Fail Watch projected that a whopping 3.6 billion people were affected by software failures last year, resulting in economic losses of over $1.7 trillion and a downtime of 268 years! There is no way you can take software testing lightly – for the end result is not just a financial loss but more significantly, a loss of name and customer trust.

The Buzz around Fuzz Testing

Among the myriad types of software testing being undertaken by developers throughout the software development life cycle, fuzzing or fuzz testing has picked up steam of late. An automated software testing technique, fuzz testing involves inputting invalid, unexpected, or random data to a software and monitoring it for crashes, memory leaks, or failing assertions. By demonstrating the presence of bugs rather than their absence, fuzz testing exposes hidden vulnerabilities in a software.

Using fuzz testing to discover coding errors and security gaps in software involves feeding enormous amounts of random data to software in an attempt to make it crash. However, running a fuzzer – which can either be a file or a protocol – even for several weeks, and not finding a bug does not certify the software bug-free; after all, the software may still fail for an input that has not been executed, yet.

Here’s how you fussy you should be with fuzz testing:

  • Automate values: Since fuzz testing relies on the assumption that there are bugs in every software waiting to be discovered, using an automated program that feeds random inputs into the software is the most appropriate way to spot them. Rather than attempting to list down values that are likely to provoke a crash – which might take you ages – an automated fuzz test inputs a large number of illogical values that a normal programmer would never think of.
  • Feed a large number of inputs: In order to spot vulnerabilities, use a fuzzer that produces a large number of inputs in a relatively short time. By using a toolchain that automates otherwise manual and tedious tasks, you can enable automated generation of failure-inducing inputs. For eg., google’s OSS-fuzz project produced around 4 trillion test cases a week.
  • Conduct dynamic program analysis: Using a static program analysis for fuzz testing might just analyze a software without executing it and report problems that actually don’t exist. Hence it is advisable to carry out fuzz testing in combination with dynamic program analysis so as to generate an input that truly witnesses the stated issue.
  • Automate bug triage: To effectively expose bugs and fix or patch the security critical ones with higher priority, you can group a large number of failure-inducing inputs based on their root cause and then prioritize each individual bug based on severity.
  • Use sanitizers: To make a fuzzer more sensitive to failures, inject assertions that crash the program when a failure is detected. You can choose from different types such sanitizers to spot different kinds of bugs: deadlocks, undefined behavior, memory leaks, control-flow, etc.
  • Employ test case reduction: In order to isolate that part of the input that is inducing the failure, a test case reduction (or automated input minimization) tool would eliminate as many input values as possible while still reproducing the original bug. This is especially helpful in cases where the failure-inducing input is large and hence proving difficult for testers to understand the root cause of the bug.
  • Avoid random plugging: If your fuzz testing program reveals several bugs in your software, plugging them as they appear may not be the right way as random plugging might reduce the robustness of the software. Consider fundamentally hardening the file format through the use of checksums, XML, and grammar-based file formats.
  • Verify everything: The most common mistake many testers make is to assume that because an instance of the software outputted the correct value once, it will output the same correct value every time. But that can be risky: the instance could have been overwritten, it could have been corrupted, it could have been modified by another program that had a bug or it could even have been intentionally altered in an effort to enhance the software’s security. Hence, assume nothing. Verify everything.

Avoid Failures:

In a world where organizations are losing millions due to software fails (UK-based Provident Financial lost $2.4 billion worth of market value), fuzz testing greatly improves the confidence that an application is robust and secure against unexpected input. Although fuzz testing is simple, it often reveals serious bugs, defects, and potential avenues of attack that must be fixed before the software is shipped. By automatically injecting random permutations of data into a target software until one of those permutations exposes a weakness, Fuzz Testing is helping testers discover software faults and rectify them early in the cycle. Just a few bytes out of place can bring the entire software product down. In such a scenario, fuzz testing helps to prevent such disastrous outcomes. And that’s not fuzzy logic.

What is Guided Unit Test Creation and How Can It Help Your Testing?

The role of testing has never been greater than today. As we move towards the age of software-defined-everything, there is no longer any room for error-prone software. Testing teams are becoming more embedded in the development process. Test automation has been embraced with open arms and has emerged as the critical driver of superior testing and increased testing proficiency. Could we even imagine increasing test coverage to the last possible component had it been done only manually? Could the speed of testing ever match the speed of development had test automation not been there? Clearly, the need for automation stems from the need to test more, test often, test fast and to ensure maximum code coverage.

What is Guided Unit Test Creation and How Can It Help Your Testing

The Unit Testing Conundrum:

Unit tests may be considered the little brats of the testing world… many in number and often difficult to manage. However, ignore these and you know that your world will soon come crashing down. Unit testing involves the testing of the smallest testable parts of an application. These components or units have to be tested independently and individually to ensure that they are operating as planned. While unit testing can be done manually, it becomes time and resource intensive and because of their exhaustive nature, can be prone to errors when not automated. These tests flow as a part of the Test Driven Development methodology which needs the developers to write failing unit tests to take into account all possible errors, inputs, and outputs. The developers then write code to test the application till the tests pass.

The problem with Unit Testing

As we lean towards methodologies such as Agile, DevOps, and TDD it becomes abundantly clear that unit testing has the power to improve project outcomes. Bugs found early in the unit testing phase are much cheaper to fix than those found later. Making testing a part of the development process ensures the success of agile and DevOps and consequently ensure high-quality and bug-free software development. However, despite these obvious benefits, the struggle with unit testing continues. Some of the reasons for this are:

  • Time-consuming and tedious test creation process – Creating a comprehensive test suite for unit tests is seen as the least attractive part of the testing process owing to the sheer number of tests that need to be written.
  • Expensive test maintenance – Any changes to the code mean changes in the associated tests. This adds to the maintenance burden of the unit tests and leads to the idea of ‘double work’ .
  • Difficulty in isolating tests – Isolating tests also means mocking all those independent dependencies. You get the drift of where we are heading here.

While test automation helps considerably in managing these issues associated with unit tests, it clearly has some issues that still need resolution. The solution to these issues may be found in Guided Test Creation, a unit testing approach that gives testers and developers the assistance they need for test creation and maintenance. All this while working within the developer’s IDE and while leveraging existing tests and mocking frameworks.

Guided Unit Test Creation – the finer details

Most of the developer IDEs do not provide the ‘content’ the developers need to complete the test creation process. The assertions needed to define a test have to be done manually. Mocking frameworks also requires manual coding and this becomes very resource intensive and time-consuming. Guided test creation simplifies this entire process and provides the real-time, context-aware assistance in the developer’s IDE. With the help of these guided unit tests, it becomes far simpler to add the missing content from the unit test skeleton. This helps in writing, conducting, and completing comprehensive unit tests faster.

The aim of the guided unit test can be summed up as follows:

  • Create test frameworks, configure mock objects, and methods as well as instantiate objects.
  • Highlight object values that changed during the testing process by performing runtime analysis of test automation execution.
  • Identify method calls that need to be mocked to better isolate test code.
  • Identify system resources that are created but not released after test completion that could create an unstable test environment.
  • Detect dependencies and automatically fill the details needed to create a mock framework thereby reducing time and resources needed to create the same.
  • Provide recommendations that detect the change in code and update the assertions to reflect the new business logic which makes test suite maintenance easier.

Conclusion:

It’s apparent that with the help of Guided Unit Test Creation, developers can resolve many of the problems associated with those pesky unit tests. Several mundane aspects of unit tests are suitably resolved with guided unit test creation. They eliminate the roadblocks that stem from the effort needed to create and maintain these unit tests too. A word of caution, however, while using a guided unit test creation tool, close attention must be paid to ensure that these tools integrate seamlessly with the existing unit testing tools and frameworks. Not doing so would eliminate the time and cost benefit. That addressed, Guided Unit Test creation could be what unit testing has been missing all along.

Testing for Blockchain – Here’s What You Need To Know

New, cutting-edge technologies are entering the marketplace at an unprecedented speed. The headline-maker on the block happens to be Blockchain, a technology that grabbed eyeballs as one of Bitcoin’s core components. Today, Blockchain is more than just Bitcoin. The blockchain is emerging as the next tech disruptor. According to a survey by the World Economic Forum, 10% of the global GDP will be relying on Blockchain-based technology by 2027.

testing blockchain

So, what is Blockchain Technology?

The brainchild of a person or a group of people using the pseudonym Satoshi Nakamoto, Blockchain is, according to Don & Alex Tapscott, authors Blockchain Revolution (2016), an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value”. The blockchain is essentially a distributed ledger, a continuously growing record list called blocks that are linked and secured using cryptography. This distributed ledger or Blockchain consists of an encrypted digital filing system that creates tamper-proof records in real-time. In other words, Blockchain can be looked upon as an open infrastructure capable of storing several different kinds of assets.

Blockchain and Testing

The blockchain is unique because it removes the need for a middleman to physically oversee transparent actions in real time while at the same time preventing fraud. Supply chain, healthcare, energy, event-ticketing, sales etc. are industries that present themselves to this technology very well. The algorithms used in Blockchain are well-established. Given that it is a distributed system, Blockchain blocks do not have a master copy and are stored in different locations. So, when it comes to testing in Blockchain, given that the algorithms used are sound do we really need to test?
A block, when added to a Blockchain, remains there forever. Any changes to one block will render the following blocks invalid. So, a single change in the Blockchain means that all the subsequent blocks have to be changed simultaneously and right away. Since this cannot be done at a later date, testing of the Blockchain becomes quite complex.

Testing Blockchain-based applications are challenging also because there is a significant change in the technology itself. For sure, Blockchain applications will demand the standard testing and validations such as functional testing, performance testing, integration testing, and security testing. But, that apart, testing teams will also need some specialized testing capabilities.

Standard functional and Non-functional testing

  • Functional Testing: Blockchain technology is finding new applications faster than before. Functional testing of the basic components, the system, and its workings is essential. Testing here is conducted to assess the effectiveness of use-case scenarios and the specific business processes involved.
  • Integration Testing:
    Integration testing is important for Blockchain since deployment could be across several systems and environments. Given this, it becomes essential to ensure that the interfaces between the components, the integrations, and the different parts of the system are functioning cohesively. This is essential to ensure performance consistency.
  • Security Testing:Security testing has to be aggressive for Blockchain applications. The aim is to identify if the application is vulnerable to attacks, assess if the authorization systems are robust, identify if the system protects the data and has the capability to ward off malicious attacks etc. Along with this, it is imperative to test integrity, authentication, confidentiality, and non- repudiation during security testing.
  • Performance Testing:Blockchain applications are built for speed. This makes performance testing even more important. The performance of an application and the latency vary with networks as well as transaction size. Performance testing in Blockchain includes identifying performance bottlenecks, defining the metrics for tuning the system, and assessing if the application is ready for production.

Specialized Testing

  • Smart Contract Testing:Smart Contract testing is a specialized testing. Smart contracts lie at the core of the Blockchain validation process. Testing of smart contracts calls for simulating all possible expected and unexpected conditions for all possible contract. Testing looks at business logic combinations and appropriate execution of all the transactions in the context of a dynamically changing and expanding the network.
  • Peer/node Testing:The power of the Blockchain lies in the shared ledger being exactly the same at each and every node with the same set of and sequence of transactions. This makes it essential to achieve a consensus across all nodes on the order in which the transactions are added to the network. Peer/Node testing for the consistency of transactions is needed. This calls for the testing of the consensus protocol to determine that all the transactions get stored in the proper sequence. This would have to be the case under normal conditions and also under conditions when nodes fail simultaneously or when they do not participate in the network for some time. These tests help ensure that the nodes in the network sync with other validating peers and the integrity of the network and shared ledger are maintained throughout.

Along with all this, testing for block size, chain size, transmission of data, and testing of cryptographical data are also essential to Blockchain applications. Given the sheer number of nodes and the various combinations and transactions that need to be validated, test automation may well prove critical to the success of Blockchain applications.

Conclusion:

The blockchain is an emerging technology, but one that has made everyone sit up and take notice. And like any new technology, how well and how comprehensively we can test will play a key role in its success and adoption and in how much we are able to participate in that success. What does your Blockchain testing strategy look like?

Agile? DevOps? What’s The Difference And Do You Have To Choose Between Them?

Any roles involved in a project that do not directly contribute toward the goal of putting valuable software in the hands of users as quickly as possible should be carefully considered.” – Stein Inge Morisbak

Does anyone remember the days when the Waterfall model was still around and widely adopted by the enterprises? Over the years most developers have stories of how they realized that it wasn’t giving the best results, that it was slow and inflexible as it followed a sequential process. Fast forward a few years and the principles of Kanban and scrum methodology organically evolved and gave rise to the Agile approach to software development –and we were all on board in a flash. Suddenly, software development teams were able to shift from longer development cycles to shorter sprints, fast releases, and multiple iterations.

How to use between agile and Devops?

But the evolution was not over, as we now know. As Agile shone a spotlight on releasing fast and often, enterprises started loving the opportunity to be more flexible and to speedily incorporate the feedback of their customers. However, this also revealed some drawbacks with the Agile approach. Though the development cycle was faster, there was a lack of collaboration between the developers and the operations team and this was adversely impacting the release and the customer experience.

This gave rise to the new methodology of DevOps which focused on better communication among development, testing, business, and the operations team to provide faster and more efficient development.

So now software development organizations face a choice –should they be Agile? Or do DevOps? Or perhaps somehow both? Let’s look at both approaches more closely, starting with filling in the essential backstory.

The Agile Approach Explained

Software Development approaches like the Waterfall model took several months for completion, where the customers would not be able to see the product until the end of the development cycle. On the other hand, the Agile approach is broken down into sprints or iterations which are shorter in duration during which certain predetermined features can be developed and delivered. There are multiple iterations and after every iteration, the software team can deliver a working product. The features and enhancements are planned and delivered for every succeeding iteration after discussions (negotiations?) between the business and the development teams.
In other words, Agile is focused on iterative development, where the requirements and solutions are developed because of collaboration between cross-functional and self-organizing software teams.

What is DevOps?

This is the age of Cloud and SaaS products. That being the case, DevOps can be defined as a set of practices enabling automation of processes between the software development and the IT teams for building, testing, and deploying the software in a faster and more efficient manner. DevOps is based on cross-functional collaboration and involves automation and monitoring right from the integration, testing, releasing, and deployment along with the management of infrastructure.

In short, DevOps helps in improving collaboration and productivity by integrating the developers and the operations team. Typically, DevOps calls for an integrated team comprising developers, system administrators, and testers. Often, Testers turned into DevOps engineers are assigned the end-to-end responsibility of managing the application software. This may involve everything from gathering requirements to development, deployment, and gathering user feedback to implementing the final changes.

How do they compare (or contrast)?

  • Creating and deployment of software:
    Agile is purely a software development process. That means, the development of software is an inherent part of the agile methodology. Whereas Devops can deploy software which may have being developed using other methodologies, based on either Agile or non-agile approaches.
  • Planning and documentation:
    The Agile method is based on developing new versions and updates during regular sprints (a time frame decided by the team members). Besides, daily informal meetings are key to the Agile approach, where team members are encouraged to share progress, set goals, and ask for assistance if required. To that extent, the emphasis on documentation is less.

On the other hand, DevOps teams may not have daily or regular meetings but plenty of documentation is required for proper communication across the teams for effective deployment of the software.

  • Scheduling activities and team size:
    Agile is based on working in short and pre-agreed sprints. Traditionally sprints can last for a week to 1 month or so at the extreme. The team sizes are also relatively smaller as they can work faster with fewer individuals working on the effort.
    DevOps can comprise of several teams using different models such as Kanban, Waterfall model, or scrum where all of them are required to come together for discussing regarding software deployment. These teams could be larger and are by design much more cross-functional.
  • Speed and risk:
    Agile releases, while frequent, are significantly less than what DevOps teams aim for. There are DevOps products out there that release versions with new features multiple times in an HOUR! The application framework and structure in Agile approach needs to be solid to incorporate the rapid changes. As the iterative process involves regular changes to the architecture, it’s necessary to be aware of every change related to the risks to ensure quick and speedy delivery. This is true of DevOps also, but the risk of breaking previous iterations is far greater in DevOps as the releases are much more frequent and follow much faster on the heels of one another than in the Agile approach.

Conclusion

DevOps is a reimagining of the way in which the software needs to be configured and deployed. It adds a new dimension to the sharp end of the value chain of software development i.e the delivery to the customers. There is some talk about that that DevOps will replace Agile, but our view is that DevOps complements Agile by streamlining deployment to enable faster, more effective, and super-efficient delivery to the end users. That’s a worthy goal –so why choose between the two!

How Analytics Will Make Test Automation Even More Intelligent?

Testing is a ripe field for applying AI because testing is fundamentally about inputs and expected outputs…… Testing combines lots of human and machine-generated data. Folks in testing often don’t have much exposure to AI, but that will change quickly, just like everyone else in the world is waking up to the power of AI.” – Jason Arbon, Author, and CEO of test.ai

We could say that automated software testing is essentially a quality control system that vets the operational aspects of a software product. The aim is to create a testing process that is rigorous and that operates through one or multiple test automation frameworks. Typically, upon completion, the tools report the results and compare outcomes with previous testing cycles. This is the age of Big Data and Analytics – it stands to reason that innovators have developed intelligent analytics solutions that offer insights designed to translate these test results into actionable information for future improvement. These solutions proactively identify problem areas in the testing process and indicate the way forward to achieve a high-quality software product. Let’s take a closer look at how analytics can help test automation.

How Analytics Will Make Test Automation Even More Intelligent?

Use of Analytics

In this context, analytics enables software developers to critically evaluate the performance of their test automation. They can track the various metrics and parameters involved in the creation of the test automation and the performance of the automated software testing exercise. Error logs embedded in the dashboard can spotlight the areas of improvement. Similarly, data about the number and the kind of functions that pass muster indicate the health of the software product that is being tested. The final status of the test results presents a perfect picture of the state of functionalities of the tested software. The graphical representations in the analytics dashboard portray a clear picture of testing outcomes that is easy to read and understand for everyone.

Predictive Analytics:

This aspect of analytics uses mathematical algorithms and machine learning technologies to forecast outcomes of software testing procedures. This technique uses current and past data to generate insights and locate potential points of failure in software testing outcomes. This enables the development and testing leaders to proactively address issues early in the lifecycle, and hence faster and easier. The use of predictive analytics also helps to detect delays and issues in software testing cycles. It also helps to monitor team productivity in testing cycles that involve human beings. Software developers can also run risk mitigation efforts when they use predictive analytics in testing procedures.

Benefits of Analytics in Testing:

Analytical reports draw on data that resides in multiple sources. This helps to present a more complete picture in real-time. The insights are clear and present, the actions to be taken are apparent, and the results can be tracked. The granular nature of the feedback generated by analytics should help software designers and testers to correct specific errors and the speed up slow processes.

The application of analytics should help software testing systems to overcome traditional or legacy limitations. The visual depiction of data in test performance and test history charts creates significant grounds to improve the testing procedures of the future. It is true that even today, automated software testing may fail for a variety of reasons, but the judicious application of analytics can increase the utility and the chances of success. In addition, interactive analytics-driven dashboards can offer enhanced monitoring and reporting capabilities for software testers and software developers. Further, analytics helps to expand the productivity of complex software testing tools while boosting the productivity of the testing team. This can help to release higher-quality products faster and more often.

The combination of test automation and advanced analytics will enable software development and testing managers to spend more time on strategic activities that drive greater business value over a longer term.

The Future of Automation in Testing:

Enterprises today are driving a relentless focus on quality. Current and future products are undergoing design changes that will make them even more intuitive and easy to use. The user interfaces will be the most critical aspect and they must be tested for reliable operation at all times. The deployment of analytics should help software developers and designers to better test software and create perfect products for clients. Intelligent observations and business insights derived from analytics will drive better, more targeted actions. Therefore, testing strategies and test plans will be refined and re-engineered to create greater scope for analytics in automation. It’s all set to be the and analytics-driven automated age in software testing – are your plans ready?

Are you ready for Robotic Process Automation?

I’d say that the biggest misconception about RPA is that it’s easy—It’s not easy. That’s why barely 10% of the buyer population in Global 2000 companies have embraced it.” – Frank Casale, founder, and CEO of the Institute for Robotic Process Automation and Artificial Intelligence (IRPA AI)

Given that warning, it would make sense to assess the internal readiness and capacity of your company before adopting or even considering RPA technology. RPA, or robotic process automation, sometimes conjures up images of a solution that helps with cognitive computing. While that may represent the ultimate level of artificial intelligence that exists, solutions that are being developed in the present focus more on automating repeatable, predictable, and fixed business processes. When we speak about enterprises, this technology is still perhaps hard to implement, but experts say that those that do adopt RPA will rapidly see the immense benefits. For companies, it could mean a winning strategy for all the stakeholders – employees included. Let us look in-depth into the possible benefits of embracing RPA.

Are you ready for Robotic Process Automation?

Firstly – what is Robotic Process Automation?

With the surge in digitization and the increasing demand on the already limited resources, the push towards adopting automation technology has now become a necessity. Technology adoption would help to empower organizations and accelerate their efforts towards gaining more business and staying ahead of their competition. Robotic Process Automation is specifically made software that is capable of simulating human conducted activities that would be routine and repetitive in nature. The software “robots” follow designated workflows, seeking information from clearly identified sources and then performing routine yet critical operations. The functions impacted could include back-office processing, support, and front office management. Today, several companies have begun using these solutions with resounding success.

Benefits of Robotic Process Automation to Organizations:

  1. RPA is best for transactional or rule-bound processes and for organizing data. Using robotic technology companies have a reduced effort and even lowered full-time equivalent employee (FTE) requirement.
  2. By incorporating RPA into the rules of business, companies would be able to simplify processes, conduct speedier transactions, maintain uniformity in their documentation, and afford complete transparency of their processes. These factors incidentally are among the top priorities for customers today, which therefore would lead to enhanced business and success.
  3. Using RPA reduces the opportunities for human error in rote tasks, and limits the exposure of sensitive and confidential information to humans – critical for the proper functioning of any company.
  4. Several companies require huge amounts of data to be added to various systems and myriad applications. Manual data entry is prone to errors especially given the mundane nature of the task. By using RPA in this process, companies can be sure of increased output of work with no errors, thereby removing the requirement of extra headcount to check and correct the errors. Re-work is eliminated as well.
  5. Since several tasks can be completed in less time and at a lowered cost, the ROI of the initiative can be achieved relatively quickly.
  6. Since RPA is a software, it has endless possibilities and capacity to work – meaning around the clock availability and work churned out. This means a lot more work with no interruptions or breaks.

Where Robotic Process Automation Stops

These RPA-driven software robots are capable of making only limited decisions and the cognitive ability is extremely constrained. In some ways, the primary objective is significantly improving the efficiency, not really the effectiveness. That’s to say, RPA allows organizations to perform these functions faster, with less effort, utilizing fewer human resources, and with greater accuracy. That said, if there is ambiguity about the inputs or if there is volatility in the functions to be addressed or the business processes are extremely fluid and dynamic, RPA is less likely to be successful.

A Bright Future for RPA

It has been said often that the downside of cognitive artificial intelligence is that it would reduce the number of jobs and dislodge employees from their roles. RPA on the other hand empowers employees by allowing them to engage in more creative and value-added tasks and removing the dull, rote ones.

Well-designed RPA solutions have this distinct advantage – companies can embrace them without the need to dismiss employees or spend too many resources in training them on the solutions. The great part of RPA is that it requires human supervision, placing control in the hands of the employees, thereby raising their morale, enhancing their development, and making them more productive on the job. Different types of organizations are already tasting success in areas like:

  • Government: Verifying existing processes; populating forms and assigning subcontractors to jobs, and integrating legacy systems with newer systems.
  • HR: Providing employee on-boarding; better managing leave of absence requests; more efficiently populating employee data into multiple systems, and offering performance appraisal management.
  • IT: Creating new accounts; installing software and updates more seamlessly, and setting up printers and workstations.

Conclusion:

It is probably time to integrate RPA solutions into your company, and the good news is technologies, including strategies borrowed from test-automation, are already at hand to seamlessly draw RPA into your business processes. Are you ready, is the question?

Challenges faced by the Agile testing team

Agile model is one of the trending and widely used software development approach based on the combination of incremental and iterative development, where software product is being continuous delivered in short and multiple cycles.

What is agile testing?

Agile testing is a testing approach based on agile principles of software development. In agile testing, continuous testing is being carried out with less or no documentation and regular feedbacks and reviews from the client/customers to improve software quality.
Further, agile testing methodology has following advantages:

  • Better and effective communication, as agile development involves the participation of developers, testers and client/customers, all at one platform.
  • With the collaboration of development team with the testing, defects are easy to locate and fix.
  • Incorporation of dynamic changing requirements.

Although, agile testing has certain advantages, which may attract a tester to follow and implement agile testing practices, but carrying out agile testing is not simple as it seems to be. Testers who have been the part of the agile testing team are very well aware of the challenges faced during agile testing.

Let’s put some light on the top 7 challenges faced by agile testing team during agile testing:

  1. Insufficient Test Coverage:The continuous incoming and changing requirements increases the chance of missing out the testing of essential or critical functionalities and features. Further, short cycles and continuous delivery mechanism of agile model may force and drive testers to consider and test only critical functionalities and leaving behind all other major/minor functionalities untested.
  2. Lack of documentation:Although, less or no documentation is one of the features of agile testing, still the non-availability of documentation work puts testing team in a difficult situation to verify and validate the software functioning in the absence of documentation work.
  3. Dynamic changing requirements:Frequently incoming and changing requirements after each iteration changes or modifies the software either in large or in small respect and irrespective of the changes brought, the system has to made go through regression testing to verify and validate the originality of the system. Thus, apart from regular testing, creating regression tests and executing them after each iteration increases the burden of the testing team.
  4. Last minute changes:Incorporation of incoming and changing requirements is an inherited feature of the agile methodology. The mechanism of the agile methodology lies itself on the fact that requirements would be evolved gradually with the collaboration of all teams including business team with the client/customers.However, when testing is about to close and product is ready for its market release, at that moment of time introduction of any more changing requirements is intolerable for the testers, especially when the deadlines are approaching.
  5. Performance parameter:As in agile methodology, software application gradually develops, complexity also increases gradually. Thus, performance issues with the software arises as the developers are not able to assess the performance attribute from end-user perspective.
  6. Tools selection:Selecting appropriate and desired automation tools for automating the tests not at early stage of development but in the later half is one of the major challenges faced by the agile testing team, as selecting tools for agile environment is much different to that for traditional approach of development.
  7. Communication:Although, development teams and client/customers collaboratively interacts after each iteration, still agile environment perceives the lack of proper communication due to absence of any effective communication medium or may be the absence of any sort of documentation.

Complete Bug Triage Meeting Process.

Let’s break this term and understand their meanings separately to derive the concept of bug triage. Bug is a common term, which you all must be aware of. In software engineering, the term ’bug’ has been assigned to flaws or errors present in the software application or programming which produces deviation in its intended functioning, resulting into incorrect or inappropriate or unexpected outcomes and results.

Now, what is triage? Wikipedia says “Triage is the process of determining the priority of patients’ treatments based on the severity of their condition”. Similar concept is applicable to bugs found during software testing also i.e. to conclude the priority of defects based on their severity. In simple words, bug triage may be defined as a process or mechanism to determine the defect priority with respect to their severity to impact the software functionalities and working.

Now, coming to the topic of discussion i.e. bug triage meeting process. As the name specifies, a project meeting held for the purpose of triage process i.e. sorting out the priority of open bugs, when it needs to be fixed, how to fix it, efforts and difficulty in retesting.

Who are the Participants in the Bug Triage Meeting?

Generally, the meeting is held by the Cross functional team consisting of QA group, project manager, QA manager, test manager, product manager and testing leads of all projects along with the participation of experienced and competent team members.

When to go for the meeting for Bug Triage Meeting?

Bug triage meeting should be carried out on a regular basis throughout the testing life cycle. Number of meetings and the gap between each of them is derived by the need and requirement of the project. Adding to this, the quantity of defects reported, time schedule and progress of the project may also paves the path for the meeting.

What are Roles and Responsibilities of each Individuals in the meeting?

  • Project Manager: Project manager is involved in the task of defect prioritization with the additional responsibilities of sending meeting minutes, tracking issues and discussing the next possible date of meeting in consultation with the QA leads.
  • Product Manager: Generally, assists the team in the process of prioritizing the defects.
  • Test Lead: Test lead or QA lead are the ones who call the meeting. They are involved in the task of defect prioritization. Further, they have the responsibilities to submit bug report to cross functional team prior to meeting, managing defects and distribution of updated bug report.
  • Development Lead: Assists in the task of bug prioritization by analysing and assessing the severity of each bug to impact system. Further, involvement of developers in the meeting helps in locating the root cause of the defects, and subsequently bugs are assigned to desired developer for its correction.

What are the activities carried out in the Bug Triage meeting?

Prior to bug triage meeting, previous status report along with the bugs found in the current phase is being sent to all concerned members by the QA Leads. Further, bug triage meeting usually involves execution of following activities:

  • Ensuring that the identified bug has sufficient and useful information to convince developer(s) it as a bug.
  • Correct filing of the bug.
  • Bug is defined on the basis of appropriate severity and priority.
  • Consideration of defects for their fixation which may be derived by following two factors:
  • If project is at its initial stage, then all bugs, even the lowest priority ones may be included for the correction process.
  • If the project is approaching deadlines or is in the final stage, then only high priority bugs, having high risk needs to be considered for the fixation process.
  • Reassigning the bug priority and severity, and accordingly updating bug tracking system.
  • Post meeting, the minutes of the meeting is being distributed to the concerned members.

Bug Triage Report Format:

Below given is a basic template of a bug triage report which may modified with the needs and requirements arises.

  • ID
  • Headline
  • Reported Date
  • Submitted by
  • Severity
  • Priority(fix)
  • Owner
  • Status

When to conclude this meeting?

Prioritizing each opened bug with the task of assigning the defects to the concerned person marks the ending of the meeting.

Conclusion:

Overall, it may be stated bug triage meeting is an effective approach to manage and fix defects throughout the testing life cycle.

Here’s Shift Left Testing – But Should You?

As we dive deeper into the software economy the role of software testing becomes ever-more important. After all, there is hardly any place for buggy or defective products in this software-driven world. Today, concepts like driverless cars, hovering drones, manufacturing automation etc. are all a reality not just because of technological advancements buts also because of the immense emphasis on the quality of testing. Testing, that was once relegated to the end of the development cycle has now changed completely owing to the rise of development methodologies such as DevOps and Agile. Now it is a key enabler of robust and solid product development. As the testing process becomes more integrated with the development process and becomes more continuous throughout the development lifecycle, we meet another testing approach. Welcome ‘Shift-left testing’.

Shift left testing approach

Shift-Left Testing – An introduction

When the software development industry was using the traditional waterfall development approach, testing was kept on the extreme right side of the software development lifecycle. This development approach looked something like this:

Requirement gathering -> Design -> Coding -> Testing

With software testing being on the extreme right, bug detection happened at the very end of the development lifecycle. As a direct consequence, the time, effort and money spent to rectify bugs and errors became enormous. In many cases, it also led to delayed product releases and missed opportunities. As organizations realized that defects and errors were less costly to fix when detected early, the concept of Shift-Left testing was introduced. This concept essentially shifted testing from the far right, i.e. from the end of the development lifecycle and was introduced at every stage of the development lifecycle.

In Shift-Left testing, testing teams collaborate with the stakeholders involved in software product development earlier in the development process. With this approach, the testing teams are able to understand requirements, expected functionalities, software design and architecture, coding etc. to gain complete product knowledge. They can draw up thorough testing plans that consider all the possible scenarios to identify software defects.

Shift-Left testing and its relevance in today’s software development landscape.

Today development cycles are becoming more iterative and involve shorter sprints. Development methodologies today such as Agile and DevOps demand faster feedback and continuous development and deployment to meet the changing needs of a volatile market. With the Shift-left approach, the development and testing teams run in tandem and ensure that all defects are fixed on the go. In fact, unless testing is involved in each step of the development process, these new methodologies will not be able to deliver on their promise – that of releasing great quality software products into the market, faster. Shift-Left testing allows software development companies release new software any time during the development process. In essence, this allows frequent product upgrades to meet the changing needs of the user.

The benefits of Shift-Left testing:

Shift-Left testing promises better software quality.

Let’s see how.

  1. Comprehensive testing from the word ‘go’:

    The Shift-Left testing approach is as rigorous as it is aggressive. Since the testing process is introduced right at the beginning of the testing process, testing teams are better equipped to create more comprehensive tests that cover every aspect of the product in production. A much-quoted study said that “56% of defects originate during the requirement phase of the project as compared to a meager 7% during the coding phase”. With the Shift-Left approach, the attention to quality begins right at the inception. Testers can ensure that the software gets tested for each and every functionality and performance aspect. Enhanced test coverage automatically translates to better quality software that is commercially more viable.

  2. Effective and faster bug resolution:

    With Shift-Left testing, bugs can be identified earlier in the development process as testing becomes a proactive contributor to the development lifecycle. The software thus is open to review and to rectification right from the beginning. When bugs are identified earlier they can be fixed faster. This consequently increases the speed of development.

  3. Better product development:

    Shift-Left testing takes the main stakeholders of product development, the developers, the testers, the business heads etc. and ensures a collaborative approach to product development. With a better understanding of what the product is expected to accomplish and who it is targeted towards, testing teams can create better and more comprehensive test plans. This ensures better quality of the product in production while eliminating the frictional differences between teams.

  4. Faster time-to-market:

    As the key stakeholders of software development work in a collaborative manner in the Shift-Left testing approach, it aids the velocity of development too. The development teams are able to resolve bugs and defects proactively in the development process. This means that the number of bugs and defects to be fixed at the end of the development process and the ensuing regression testing to ensure that all connected parts of the software are operating as designed is reduced considerably. This ensures speed of development -the products can be released in the market faster.

Conclusion:

The idea of Shift-Left testing is not just to introduce testing earlier in the development process. It is also about combining the right set of tools, methodologies, frameworks, and approaches to enable predictability, and the detection and prevention of defects from the beginning to the end of the project. This enables agility and boosts productivity. The result should be a better product, released faster, and eventually greater profitability.

Software Trends You Should Be Excited About In 2018

2017 was an exciting time to be in the software product development and testing space. We witnessed the rise of DevOps as a solid development methodology, saw Agile become more mature, and the establishment of test automation as an essential to the success of any software development methodology. Iterative software development emerged as the enabler of delivering high-quality software and this resulted in generating greater customer satisfaction. 2018 promises to be no less transformational in the world of software development. Here’s a list of things that we are, and you should also be keeping an eye on in 2018.

Software Trends You Should Be Excited About In 2018

  1. Artificial Intelligence:

    While AI has made its presence felt in our lives in the form of Siri and Cortana, 2018 will witness AI influencing development and testing too. Right from conceptualization to software development and testing, and deployment and maintenance the AI impact is expected to be quite pronounced in 2018. Using AI, developers can create better software by creating richer functionalities that are more user responsive. The use of AI in software testing is also going to be quite tangible. With the use of AI, testing teams will be able to better optimize test automation cases, validate hard to process artifacts, simplify complex tasks, and create intelligent test scripts that are adaptive in nature -amongst other things.

  2. Progressive Web Apps:

    2018 is also looking like the year of Progressive Web Apps. Introduced by Google in 2015, PWA’s got a lukewarm reception in 2016. 2017 saw these apps gradually gain momentum as they presented a potential alternative to designing a native mobile app. Presently there are over 6.5 billion apps for download while the corresponding number for regular app use is on the decline according to Statista. As the number of mobile apps increase but lose their appeal, PWA’s present themselves as the next big hope for the mobile web. PWA’s makes use of the latest technologies to combine the best of web and mobile applications. These are more reliable, load quickly, respond faster, and provide an immersive UX all while being more maintainable.

  3. Single Page Application (SPA):
    Single Page Applications or SPAs continue to climb up the popularity charts. Initially used to build the private dashboard portions of SaaS platforms or Internet services, SPAs based on JavaScript will continue to rise in popularity in 2018. This is driven by the sense that they are high performance, reduce development time, and are device agnostic. While it still might be too early to assess the likely success levels, the growing popularity of SPAs in the development circles is already visible.

  4. Automation and Continuous Testing:
    Continuous testing is all set to become an overarching theme in 2018. This is owing to the accelerating shift towards DevOps, continuous delivery, agile etc. With this, we will see an increase in automation in the software testing space. We are sure to witness test automation become a first-class citizen in the age of digital transformation. As continuous delivery pipelines along with running software and services become an omnichannel business, software development and testing companies will want an even greater convergence of testing tools. In 2018, we can expect to see this convergence of functional test automation tools with mobile front-end test automation tools. These companies will no longer want to use one tool for running functional tests for browsers and other environments and another for mobile environments.
  5.  Motion UI:
    Web design trends in 2018 are all about being more cutting edge. As web design becomes more dynamic, smooth animation becomes a design imperative. In 2017, we saw Motion UI gain rapid popularity. This was because it gave developers the capacity to animate content faster and with greater ease without any in-depth knowledge of jQuery or JavaScript. Motion UI helps in capturing the attention of the user by easily adding movement and drama elements to pages.

Conclusion:

Along with all this, we could also expect to see the further evolution of strategies to engage the users more. An example in 2018 could be chatbots based on artificial intelligence to increase the efficacy of online communication.
2018 promises to be an important year in the software development business as software begins to touch our lives and work in every possible way. Clearly, technology is all set to embed itself in everything we do as almost all businesses become software driven. As the way people and companies use software changes – can the way we develop software afford to stand by the wayside and ignore the change?

There’s a Transformation Underway in Test Automation – Here’s What To Look For

The change in software development methodologies has not only impacted the manner in which software products are developed but have also turned software testing and QA on its head. Today, testing is not a solitary function anymore. Organizations now realize the role of great testing in the development of robust, high quality, and error-free software. This has increased the potential impact area of testing. As applications grow in complexity the test automation market experiences change as well. Here’s a look at the transformation that is underway in test automation:

Transformation in Test-Automation

  1. Increased Focus On Mobile Testing:
    With the number of mobile devices increasing consistently and the consequent ‘appification’ of the economy, the focus on mobile testing is only going to increase. Mobile application testing will continue to focus on performance, functionality, usability, compatibility and security testing. As the market for mobile devices continues to expand, mobile test automation will likely out-run cross-browser test automation. The growth of the open-source frameworks, the emergence of Mobile DevOps, and the app economy will contribute to the expansion of mobile test automation.

  2. Shift-Left Testing: 
    Shift-left testing is on its way to becoming a standard in the testing industry. As development mythologies such as Agile and DevOps mature, as does the complexity of software products and applications, the need to test early and test often become critical. We are increasingly witnessing software organizations lean towards Shift-Left testing where the testing process starts early in the development cycle. The objective of Shift-Left testing is to start testing right from the requirement phase itself to reduce the cost of bug identification. The adoption of Shift-Left testing is also dovetailing into Test Driven Development and Behaviour Driven Development. The aim? To develop error-free products without delays.

  3. The rise of Microservices testing:
    Instead of developing monolithic pieces of software, organizations are developing software that comprised smaller pieces. This independent and decoupled architecture a.k.a Microservices reduces inter-modular dependencies and enables faster releases along with ensuring scalability and manageability. Testing these applications can become quite complex. Hence the demand for enhanced levels of test automation and an increased focus on API testing.

  4. Focus on testing of SOA architectures:
    Testing of the web services and SOA architectures in today’s age of complex application integrations has become extremely important. Test automation initiatives have to focus on checking the end-to-end performance of applications. The aim is to ensure that the interconnected systems and parts communicate efficiently with one another and generate the expected response.

  5. End-to-end lifecycle automation:
    Test automation is ready to move out of the confines of functional test automation. There is a rise of web services, API’s and a growing data reliance. This demands end-to-end lifecycle automation. Automation of everything across the entire software lifecycle is becoming a necessity.

  6. Unification of collaboration tools:
    With collaboration becoming an imperative in the software development lifecycle, the growing need is for speed as well as agility. Developers, QA, operations engineers, and testers are looking at a unification of collaboration tools. Instead of using a separate set of tools according to the roles, to increase the effectiveness of testing initiatives and improve collaboration between teams, these teams will now be able to use the same set of tools and IDE’s.

  7. The DevOps impact on testing:
    Testing is increasingly feeling the DevOps impact. And this is only going to grow over the next year. We can expect to see a greater merging of roles of testing experts with developers and operational engineers. This is due to the rising need for collaboration in developing superior quality products and to accommodate the continuous agile cycles. This will also demand an increase in test automation as the need for continuous updates for quality and continuous integrations, rises in the development lifecycle.

  8. The move from performance testing to performance engineering:
    In order to drive user experiences, performance testing will make a shift towards performance engineering. The focus will be more on ensuring consistent application performance across different platforms, mobile devices, and operating systems. The demand for better user experiences and an increased focus on UX will further drive this shift from performance testing towards performance engineering. This approach will not only confirm that the software meets the performance requirements but will also ensure that the cost of development goes down.

  9. AI meets test automation:
    The adoption of Artificial Intelligence is gaining momentum. Software testing too is expected to become a playing field for AI. The use of AI in test automation will be to make the testing suites more intelligent, validate hard to process artefacts, automatically create test scenarios, and help test automation tools develop a learning approach. AI can do the heavy lifting in test automation and help testing teams generate possibly 100 times more test coverage and improve app performance by increasing testing performance.

Conclusion:

2016 and 2017 witnessed some major defining moments in the software testing landscape. We saw the role of testing become dominant in the development lifecycle. It has now been widely accepted that software testing and development cannot function in isolation if the end objective is to create a reliable and robust software product. In 2018 we can expect testing and test automation to continue with this evolution as new technologies and development methodologies emerge. The question for those of us in the software development game is – will we be ready?

The 5 Secrets Of Succeeding at Test Automation.

The report Testing Trends in 2017 – A Survey of Software Professionals” showed that an increasing number of development teams are deploying software faster – 14% doing so hourly, up from 10% last year. Clearly, to enable this pace of deployment the speed of testing has to increase too so that bug fixes can be faster and the feedback loop can be shortened. Perhaps, the prime enabler of faster testing is test automation and hence software development companies are focused on strengthening their test automation initiatives. Reports are that the test automation market is all set to expand at a CAGR of 15.4% from 2017 to 2025 to hit US$ 109.69 billion by 2025.

Having said this, test automation is no magic wand that can be simply waved to cure all testing related ills. Automation initiatives also demand investment, so there is increased pressure on organizations to ensure the ROI of these initiatives. In this blog, we look at the 5 secrets to ensuring test automation success.

  1. Align testing with business goals:
    First, it is essential to align testing with the expected business goals of the software application or service under development. Taking a requirement-driven approach that addresses all functional and non-functional needs of the software, and discussing these needs with the development team is essential to develop a relevant testing suite. Testers must also ensure maximum code coverage through smart test design that not only tests the boundary considerations using multiple test cases but also ensures thorough and detailed test coverage of the codes that implement the requirement.
  2. Optimal utilization of all testing and QA assets:Manual testers, automation engineers, domain experts, and product owners are also key QA assets along with test cases, test data and the testing infrastructure. While many might feel that manual testers are no longer relevant when test automation is implemented, this is not true. There are certain tests such as exploratory testing that can only be done by manual testers. Remember that test automation cannot test for everything. It is essential to rely on manual testers to identify problems at a contextual level since automated scripts are restricted by boundaries. Similarly, automation engineers should be employed to ensure that the right test automation technologies are being used, the scope of automation is well-defined and that the test preparation is such that it hastens the testing process.
    Testing teams should also take into consideration the expertise of domain experts and product owners. They can give a deeper understanding of how the user wants the software to perform and what needs it must fulfill. Test cases and test data are other areas of focus that improve the quality of test automation initiatives by ensuring comprehensive coverage of all testing scenarios. It is essential to pay close attention to the testing infrastructure for better software testing, downtime management, and utilization management.
  3. Focus on ‘what’ to test as much as ‘how’ to test:Some test automation initiatives fail because organizations look at achieving 100% automation. For the success of test automation, testing teams need to first identify the right candidates for automation. As a thumb rule, testing teams should identify those that are repetitive in the development cycle, identify the development environment, and validate the functionalities across this environment. Those tests that are repeatable and have to be done often such as functional testing, regression testing, unit testing, integration testing, smoke testing, and performance testing are more likely automation candidates.
  4. Treat the test suite like a product:To stay in step with today’s dynamic business environment, organizations have to keep product evolution in mind. This suggests that as the software product evolves the test suite has to evolve too – just like a product would. Therefore, testing professionals should analyze their test suite carefully and identify test plans that will stay relevant in the long run and which test plans will become redundant. Changing the entire test suite in the event of a product upgrade is impossible. Instead of having a monolithic test plan it makes greater sense to have more modular test plans. A modular test plan that is built using smaller and independent test cases ensures that if one test fails then the entire test suite does not come tumbling down and that if something breaks in one test then only that one segment can be changed and you don’t have to change all the scripts associated with it. Along with this, testing teams should also focus on the maintenance needs of the test automation suite and chart its lifecycle to determine its maintenance needs. Testing teams should also focus on creating automation suites that are resistant to changes in the UI to ensure that the suite can work with future versions of the product.
  5. Integrate testing with development:The aim of test automation is to speed up development, increase code coverage, and assist in keeping timeline overruns under control. To achieve this, it is essential to place testing at the heart of software development for better testing and faster delivery. As more and more organizations are adopting development methodologies such as DevOps and Agile, it becomes all the more essential to be ready with all the components of your test automation strategy before the development process begins. This will ensure the success of the test automation initiative and that the final product matches the expectations of the user.

In closing, here’s a bonus tip! Testing teams should not be lax when designing the testing code as the quality of your testing code will impact the testing process. b, robust, and quality code will ensure that the testing code becomes an asset for future use while ensuring the success of the existing test automation initiative.

Now that you are equipped with 5 secrets to Test Automation success, it’s time to go out and look at your initiatives – and make them work for you!