Abstracts

For an evolving software, code changes are an integral part in Software Development Life Cycle & one of the important tasks in Continuous Integration & Continuous Deployment. Eventually the test suite also increases with every iteration or increment of the software development. But every time a new build is generated due to code changes, the testing team needs to prioritize test cases from master test suite based on different criteria which tries to cover defect prone areas. This is a humongous & daunting task as each code change can cause repercussions on the application. This a hindrance to provide early feedback. In our work, we have developed a tool to recommend a set of prioritized test cases to perform regression testing by exploiting some of the concepts of Natural language processing by performing intent matching between test cases and historical defects in the product. This increases the productivity of the testing team which in turn in results in lesser time to market & better-quality product. The tool is already in use and able to detect bug very early in the software development process, In our presenation, we would like to describe our solution and also would like to give light on topic of "AI for software testing".