Enhancing Software Testing with AI: Integrating JUnit and Machine Learning Techniques

Abstract

This research work discusses the application of AI and ML techniques in combination with JUnit, a widely used testing framework for Java applications. With the increasing complexity of software systems, many times the traditional methods of testing fail to keep pace. The current study therefore tries to elaborate on how JUnit can be used with AI and ML to increase test coverage, efficiency, and overall testing effectiveness. We discuss various machine-learning algorithms and their application toward test case generating, test suite optimization, and defect prediction. Depending on the development context, different test suites are synthesized, including hybrids of human- and AI-generated test suites. It presents the discussion of challenges and limitation of such an approach, and in this way gives a balanced view on the state of the art and the future potential of what we see within AI-enhanced software testing.