A Value-preserving Approach to Regression Test Selection in Agile Methods

This thesis introduces a requirement-based regression test selection approach in an agile development context. Regression testing is critical in ensuring software quality but demands substantial time and resources. The rise of agile methodologies emphasizes the need for swift, iterative software delivery, requiring efficient regression testing. Although executing all existing test cases is the most thorough approach, it becomes impractical and resource-intensive for large real-world projects. Regression test selection emerges as a solution to this challenge, focusing on identifying a subset of test cases that efficiently uncover potential faults due to changes in the existing code. Existing literature on regression test selection in agile settings presents strategies that may only partially embrace agile characteristics. This research proposes a regression test selection method by utilizing data from user stories—agile's equivalent of requirements—and the associated business value spanning successive releases to pinpoint regression test cases. Given that value is a chief metric in agile, and testing—particularly regression testing—is often viewed more as value preservation than creation, the approach in this thesis demonstrates that integrating user stories and business value can lead to notable advancements in agile regression testing efficiency.