AI & ML Driving Software Testing

Here’s a little teaser to tickle your grey cells: There is a couple that goes hand-in-hand, making waves in the digital world, and has been becoming increasingly influential. Any guesses? The answer is AI & ML aka Artificial Intelligence and Machine Language. These impersonal terms are significantly impacting our personal, professional and social lives. If there is one area that is progressing at break neck speed, it is the digital space and all that it encompasses. With great strides in software development, came the need for fast paced software testing without any compromise on accuracy and security. This needed super-human capabilities. To the rescue came Test Automation driven by AI & ML.

Not so long ago software development and testing followed the Waterfall model which is a sequential development process that flows like a waterfall through all phases of a project viz. Analysis, Design, Development, Testing and Operations. Each phase depended on the deliverables of the previous one and testing in the earliest years was done manually at the end of the development phase. But as time went on, there was rapid change in digital technology, driven by ever-changing customer preferences and increasing competition. Where apps are concerned, superior user experience is the name of the game and there is a virtual race to get the app and its frequent updates to the market before competitors do! Manual Testing was just not able to cope with the speed and accuracy demanded by this fast changing world. This necessitated a move towards Automated Testing. In due course the Waterfall model gave way to the Agile Methodology that promotes continuous iteration of development and testing throughout the Software Development Life Cycle (SDLC). In this scenario of concurrent development and testing, AI & ML came as the much needed respite to the testing community to bridge the dichotomy between speed and accuracy.

In simple terms, AI-driven automated testing is a software testing technique in which AI and ML algorithms are used to effectively test a software product, by employing logical reasoning and problem-solving methods, to improve the overall testing process. ML is a field of computer science that uses statistical techniques to give computer systems the ability to ‘learn’ with data, without being explicitly programmed. Thus AI & ML reduced dependency on highly trained programming experts who were scarce to come by and also eliminated issues related to test data preparation, environments, frameworks, tools, integration and a host of other issues.

With this brief introduction to AI and ML, let’s move on to understand how AI & ML drive software testing and help software teams.

The self-testing and self-healing features of AI & ML testing systems are a great boon because the self-testing feature ensures that scans are regularly run, reports automatically generated and communicated to the software personnel along-with the possible solutions. Furthermore, the self-healing feature helps these systems to even do the needful to correct the errors! This takes off a load of responsibility from software testers, thus tremendously reducing their stress levels.

The well-defined algorithms in AI and ML driven testing, are based on logical reasoning and problem solving methods and this brings great confidence to the quality of testing. AI & ML are a great way of improving accuracy by eliminating errors associated with human fatigue, boredom of repetitive tasks, negligence etc.

These testing systems counter the triple whammy of ever-changing user preferences, rapid changes in technology and the need for extremely quick turnaround time. By replacing the time consuming code based system, AI and ML come out to be sure winners in the software testing speed and accuracy race. One of the secrets of this speed is that time-guzzling repetitive tasks are automatically identified, recorded and modified. The natural corollary is that with this intelligent automation, human efforts and cost of testing are tremendously reduced, not only in terms of initial testing but also in terms of avoiding reworks caused by escaped bugs.

With AI and ML testing systems productivity can increase by up to 5 times! Now that’s a huge fillip to the organization’s bottom line. Repetitive tasks are often the sword of Damocles hanging on the head of software testers because it’s here that errors silently creep in. By transferring these tasks to the care of AI & ML, software testers can breathe easy and focus their time, skill and energy to tackle the more complex areas of the projects. Thanks to the efficiency of AI & ML, there’s improvement in testing productivity as well as effective utilisation of testers’ time and talents, thus bringing efficiency to the SDLC.

AI and ML driven testing, brings automatic scanning, testing and checking of file contents, data tables, memories, and internal programs. Its self-testing and self-healing features greatly contribute to enhanced test coverage as well as better quality testing, in turn augmenting confidence in the testing results.

AI has also been making inroads into testing of visual aspects of apps. These were areas earlier reserved for manual testing. However, AI is now equipped with pattern and image identification abilities that help detect UI controls irrespective of size and shape, and can also analyse them at pixel level. This helps identify visual bugs and ensures the desired performance of the app’s visual aspects.

Last but certainly not the least, AI and ML ensure the ultimate goal of the software team – to win the Go-to-market race without compromising on accuracy, safety and security. With AI & ML, Concurrent and Continuous Testing, Regression Testing, etc. become possible, enabling early detection and fixing of bugs and reducing the SDLC. All this translates into achieving the crucial goal – reaching the market before competitors!

To sum up: It can clearly be seen that AI & ML are truly driving Software Testing today. With the frequency with which updates are necessitated, and the severely crunched testing time, it is not humanly possible to do justice to app testing. AI & ML come to the aid of testers to reduce their stress and enhance their productivity, sparing them of the mundane tasks and helping them focus on the more complex issues of software testing. By improving test accuracy and coverage and tremendously boosting speed, AI & ML bring in long term economies, reduce app downtime, avoid the costs and embarrassment that come with it, and prevent loss of business.

The good news is that with AI & ML driven testing even those without great programming skills can have a successful career in Software Testing. If you feel drawn towards this promising career, then visit www.mypathglow.com to learn how to enhance your prospects in just 3 months! Sign up for PathGlow’s Full Stack Software Testing Course and see your future take off to a great start.