Machine Learning (ML) and Artificial intelligence (AI) is rapidly changing the world of work, and the field of software testing is no exception. While some people worry that AI will lead to job losses in testing, the reality is that AI is more likely to enhance and augment the work of test engineers, allowing them to be more productive and effective.

It’s hard to overestimate how significant this shift will be for testing and quality assurance professionals. AI will allow for more comprehensive test coverage, higher accuracy in tests, earlier detection of issues, and with improved collaboration between humans and machines, a leap in test execution and efficiency is dawning.

This newfound ability to leverage AI will enable testing teams to move away from traditional manual testing approaches that are often time-consuming and error prone. Instead, they will be able to harness the power of machines to automatically generate test scripts, execute them at scale, and provide instant feedback on results – all while freeing up human brainpower to focus on more strategic tasks such as discussing features and performance with product owners, adopting and employing Test Driven Development (TDD) and analysing results.

Moreover, it will also improve the accuracy and efficiency of testing. By using machines to handle tedious and repetitive tasks, engineering teams can avoid the pitfalls of human limitations, mistakes and rework – key culprits affecting velocity of execution and morale. This helps to prevent friction in the process and ultimately bugs from reaching production, saving companies time, money, and, perhaps most importantly, reputation.

One key area ML is providing significant lift to QA teams is in script maintenance.  This effort generally accounts for up to 40% of a QA engineer’s time.  Smart script tagging along with attribute scoring supported by optimization models embedded in ML algorithms, allow teams to leverage ML to uncover more stable scripts and heal scripts that become brittle over time – thus reducing maintenance around 80%.

Another incredible way AI plays a role in automated quality assurance is through coverage. Instead of manually reviewing each and every step (or possible step) through an application, a team of bots will quickly scan through code to identify bugs – finding every action, every flow, every page/state/timings/etc all orchestrated with trained algorithms. Once trained, the machine will never forget a rule or validation.  And with Machine Learning algorithms, introducing a change in the logic in the application under test will not mean hours of test script rework.

In addition to improving software quality, AI in the form of Image Recognition can improve the user experience by executing test cases involved in the User Interface.  Visual approval of web design, style sheets, and objects on the page can all be tested with this new AI technique. Absent AI, these tests are difficult to automate, generally requiring human intervention to make decisions regarding design compliance.

The future of quality assurance lies beyond test automation and within the realm of autonomous testing, which entails using AI/ML for automated creation, maintenance, and execution of tests. It is clear, in the future this approach will be widely adopted to elevate software testing to the next level.  Autonomous testing involves training algorithms to generate test suites that can account for all the possible scenarios that can possibly execute in the software. While this strategy is in its early stages of adoption, there is ample evidence of its promise as a real game-changer in software quality assurance.

Conclusion

The use of Machine Learning (ML) and Artificial Intelligence (AI) in testing has the potential to revolutionize the testing process. The application of autonomous testing can give various benefits, including time savings, cost savings, increased accuracy, and overall efficiency. By using ML and AI to automate the testing process, teams can identify errors and possible issues far sooner, lowering testing time and resources. Furthermore, autonomous testing helps reduce the problems of limited resources, tight deadlines, and tester burnout, allowing teams to focus on more strategic tasks.

Mammoth-AI provides QA-as-a-Service (QAaaS) – a CICD service solution that leverages ML and AI to support testing at speed and scale for agile development teams. We create, run and maintain test scripts and leverage autonomous AI testing on your behalf for one low monthly fee. Hence as ML and AI continue to evolve and improve, the future of testing looks promising, and those who embrace these technologies will undoubtedly enjoy a competitive edge.