As Artificial Intelligence continues to expand, it is crucial to monitor the most recent developments constantly. These innovations have the potential to revolutionize a number of industries and provide both businesses and individuals with significant advantages. Two decades back, humanity didn’t know that AI would be the main driver of emerging technologies, and for the foreseeable future, it will continue to lead the way in technical innovation.
Similarly, software test automation, which is being transformed by AI, is one area where we have witnessed significant progress. AI-based testing is like having a smart assistant that helps you test your software without you having to intervene. Just like how a personal assistant helps you manage your tasks and schedule, AI-based testing tools can help you manage your software testing tasks like – unit testing, integration testing, system testing, acceptance testing, regression testing, performance testing, and security testing.
Software testing is a typically labor-intensive procedure that calls for a team of dedicated testers to test cases and spot flaws manually. That’s because each sort of testing activity is crucial in its own right and contributes to ensuring that the software complies with specifications, works effectively, and is safe. But AI is revolutionizing this with the advent of testing tools post which this procedure has undergone a complete transformation, becoming quicker, more effective, and more accurate. It focuses on critical areas, using machine learning to refine the testing process, and generating new test cases to cover untested areas.
A number of factors hampered software test automation prior to the development of an
AI-powered testing solution. Some of the biggest challenges being solved by AI test solutions are:
- Limited test coverage: Since manual testing required a lot of time and effort, it was challenging to completely test all of a software system’s features and functionalities.
Just the most crucial components of the software were typically tested as a result that led to frequent poor test coverage. - Lack of scalability: Manual testing was not scalable and it was difficult to run tests simultaneously across several devices, platforms, or environments. This limited the software’s efficacy by making it challenging to test it under many circumstances and scenarios.
- Human error: When performing manual tests, there were possibilities of human error. Testers could overlook critical scenarios or miss defects. They could also fail to see important cases. This frequently led to defects being released into production, requiring expensive rework and possibly harming the brand’s reputation.
- Maintenance issues: One of the major maintenance issues with manual testing was keeping test data and test scripts up to date. Test scripts are required to be updated when the software system changed in order to maintain their usefulness and efficacy. This frequently needed a lot of time and resources, resulting in delayed testing and release cycles.
- Execution takes time: To ensure that the software was adequately tested, manual testing was a time-consuming procedure that frequently required numerous cycles of testing. Long testing and release cycles resulted in an outcome that may affect the gestation period to launch the product.
AI is transforming software test automation by leveraging NLP techniques to analyze customer feedback in real-time, identify software improvements, and address any anomalies in real-time. Additionally, AI automates the development and execution of test cases, expediting testing and minimizing human errors. These two instrumental breakthroughs not only enhance software quality and customer satisfaction but also boost the efficiency of the testing process.
AI-led digital transformation in test solutions typically starts with a gap analysis in the existing digital environment of a company and a step-by-step strategy to fix bottlenecks in efficiency through the use of AI. When a prominent luxury retailer implemented this technology, they found that the outcomes included a greater than 50% reduction in test times, a 60% increase
in functional coverage, and a time savings of more than 600 hours each test cycle. By offering test automation, the company’s Buy Online, a Pick Up In Store campaign increased sales by $6 million. The analysis and implementation were done by Prolifics, a global leader in the digital transformation of operations.
In conclusion, testing teams can save time and effort by employing machine learning algorithms to find flaws instead of manually finding and fixing problems. AI-based test automation is a metamorphic change in the field of software testing. This is because it changes how testing is carried out while still achieving the fundamental goal of assuring the functioning and quality of software.
This article is written by Mr. Sarat Addanki, Head of Client Success, Named Accounts, Prolifics, and the views expressed in this article are his own
