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Software regression testing is an essential testing practice that ensures that changes made to the source code of an application do not affect its functionality and quality. Within this research, we introduce a novel method for prioritizing software test cases using a fusion of reinforcement learning and hidden Markov model to enhance the efficiency of the testing process. The primary objective of this research paper is to maximize the likelihood of selecting test cases that have the highest priority of uncovering defects in new code changes introduced into the codebase. To assess the efficacy of our suggested methodology, we experimented on the test cases of five web applications. Our results demonstrate that our proposed approach can accurately identify critical test cases while minimizing false positives, as evidenced by an F1 score of 0.849. This outcome can help prioritize testing efforts, saving time, and resources while improving the overall efficiency of the testing process.

Prioritizing software regression testing using reinforcement learning and hidden Markov model