Abstract In agile software development, regression testing is an ongoing process activity. However, real-world time constraints necessitate selecting a subset of tests to run. Current regression test selection algorithms primarily focus on technical metrics such as requirement coverage while overlooking the business value each test validates. This study reframes regression test selection as a multi-objective optimization problem in which tests are selected to maximize business value within a constrained testing time while maintaining adequate requirement coverage. We apply an artificial intelligence-based search algorithm, and our results show that the proposed method consistently selects tests with higher business value than baseline approaches when time is limited, while maintaining comparable requirement coverage. These findings suggest that a driven value-aware selector can be incorporated into agile teams for decisions on allocating limited regression testing resources.

Agile software development emphasizes rapid delivery of incremental features, raising concerns about software testing quality before release. Regression testing ensures new code modifications do not cause defects in already delivered features. The challenge is ensuring new code modifications do not break existing code after stopping the delivery pipeline. The research community is adapting existing regression test selection and prioritization algorithms to account for agile-specific process attributes such as time and value. The research aims to formally describe how regression testing is incorporated within the context of Agile Software Development. Additionally, the research aims to develop Regression Test Selection and Prioritization algorithms using Machine Learning techniques tailored to the characteristics of agile software development. The goal is to revolutionize the current approach to Regression Testing in Agile Software Development and deliver high-quality software within stringent timeframes.