Prioritization of Regression Tests using Singular Value Decomposition with Empirical Change Records

During development and testing, changes made to a system to repair a detected fault can often inject a new fault into the code base. These injected faults may not be in the same files that were just changed, since the effects of a change in the code base can have ramifications in other parts of the system. We propose a methodology for determining the effect of a change and then prioritizing regression test cases by gathering software change records and analyzing them through singular value decomposition. This methodology generates clusters of files that historically tend to change together. Combining these clusters with test case information yields a matrix that can be multiplied by a vector representing a new system modification to create a prioritized list of test cases. We performed a post hoc case study using this technique with three minor releases of a software product at IBM. We found that our methodology suggested additional regression tests in 50% of test runs and that the highest-priority suggested test found an additional fault 60% of the time.