Leveraging Generative AI for Automated Test Case Generation: A Framework for Enhanced Coverage and Defect Detection
Keywords:
Generative AI, automated test case generation, software testing, defect detection, machine learning, human-in-the-loop, test coverageAbstract
The assertive shift in the Software Development Processes (SDP, henceforth) requires faster and more-flexible testing. Since automated test case generation is a generative modality, the way for extending the test coverage and discovering more defects is different from the traditional one. This article discusses the incorporation of generative AI at various levels of software testing and the importance of AI-based test case design for learning from the defects introduced during code changes. It gives a complete architecture that also provides a human-in-the-loop mechanism, in that the AI algorithms must work about the testers and not generate the results in isolation. In addition to test coverage, defect detection, and execution efficiency, metrics relevant to performance effectiveness are also mentioned. At the same time, the paper discusses issues related to the challenges posed by data, the software to be tested, and, in some cases, the classical black-box AI systems. The outlook for developing testing systems based on artificial intelligence is formulated, emphasizing the explainability of AI systems and continuous changes in the processes within the agile environment. These comments are meant to help organizations leverage AI to make better software.
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