How to Stay Ahead in The Software Testing Race?

Software testing has evolved by leaps and bounds with the evolution of development methodologies and market demands. Organizations are left with no choice but to up their testing game to deliver high-quality products.

Does that bother you too? If so, let us look at some trends that will help you stay ahead in the software testing race:

AI and ML-Backed Intelligent Test Automation

Artificial Intelligence and Machine Learning are becoming ubiquitous in the value they deliver to enterprises. It’s been said that all businesses today are software businesses. And sure enough, they are looking to leverage AI and ML to power their software testing and derive maximum ROI in the end. This approach is called Intelligent Test Automation and helps achieve extreme quality objectives.

For instance, AI can prioritize your testing and automation process, enhance UI testing, reduce the burden of analysis tasks that take up time, and even generate and optimize various test cases.

Machine Learning, on the other hand, can help identify unique and redundant test cases, perform predictive analysis, and identify test cases that can be automated. It can also identify the high-risk areas that can be used to prioritize regression test cases.

Big Data Testing

When you are dealing with a huge volume of data that needs to be processed at a high speed, how do you go accomplish testing in scale?

Well, go for big data-driven testing wherein you will have to verify that the terabytes of data are appropriately processed. This can be done using various components such as the commodity cluster. This type of testing can also play a role in performance and functional testing. It takes into consideration the quality of the data, which is to be checked based on factors that include but are not limited to – data duplication, consistency, accuracy, validity, data completeness and so on.

Performance Engineering

A key approach to consider to stay ahead in the software testing race is to make the shift from performance testing to performance engineering. This is because many performance issues are designed into the software. Developers often have no clue about the likely bottlenecks as they get coding. Moving to a performance engineering approach, building in, and testing for, performance measures from the design stage itself will help the software delivery all the way down the line.

An interesting extension can be achieved by integrating agile processes with performance engineering methods. This will help address performance issues early in the development process. Also, it will help reduce rework in the latter stages, saving time and cost.

IoT Testing

Around 29 billion connected devices will be in play by 2022, of which around 18 billion will be related to IoT. IoT Testing or Internet of Things Testing is a variant of testing that is specifically done to check, validate, and certify the devices used in IoT solutions. This has become the need of the hour as the world gets connected using a growing number of IoT devices. Smart city anyone?

IoT testing includes usability testing, compatibility testing, reliability and scalability testing, data integrity, security, and performance testing. This would also cover testing the analytics, devices, networks, the operating systems, the platforms as well as standards.

It’s also important to test the solutions for devices. These differ in shape, form, platform, hardware configuration and so on and testing touches all those aspects. Since data is also an important factor here, it becomes key to perform data-related integrity testing as well.

DevOps Testing

Traditional QA used to include a build, which was deployed in a designated environment. After this, the QA would start the functional and regression testing. This led to the build sitting with the QA for some days before getting delivered. Of course, DevOps has changed this. Here’s what you must keep in mind for DevOps testing:

  • Ensure that the test environments are all standardized
  • Automate the deployment on the QA set-up
  • Automate the pre-testing tasks, cleanups, post-testing tasks
  • Align the tasks with the Continuous Integration Cycle
  • Add test cases to the QA repository
  • Contribution from people in various roles is important
  • Test execution should ideally be lean
  • Execute tests in parallel to save time
  • Come up with an exit-criteria for each run
  • Any critical bug needs to be reported and fixed, and then passed through the same chain of events
  • There shouldn’t be any lack of coordination between the functions in a deliverable chain.  


Integration of Tools and Activities

This is the collaboration age. And using a testing tool, which is not integrated with other tools can impair your software testing efforts. It is highly advisable to integrate the tools and testing activities for all the development phases. This will help gather multi-source data. That, in turn, will help effectively apply the AI and ML approaches into the testing process. Obviously, the need will be to gather the data not just from the testing phases, but also from the requirements, implementation and the design phases.

Software testing has been an important function for a long time. But as technology advances, it must evolve. Unprecedented changes are driving the digital transformation of enterprises and technology organizations. It’s only natural that the change should impact the world of software testing too. Have questions or queries? Leave a comment to get in touch!


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