Mathematical Optimization for the Test Case Prioritization Problem

Abstract: Regression testing is the process of testing software to make sure changes to the software will not change the functionality. With growing test suites theneed to prioritize arises. This thesis explores how to weigh factors such as the number of fails detected, days since latest test case execution, and coverage. The prioritization is done over multiple test systems, software branches, and over many test sessions where the software can change in-between. With data provided by an industrial partner, we evaluate different ways to prioritize. The developed mathematical model could not cope with the size of the problem, whereas a simulated annealing approach based on said model proved highly successful. We also found that prioritizing test cases related to recent codechanges was effective.