GASSER: Genetic algorithm for teSt suite reduction

Abstract

Background. Regression testing is a practice that ensures a System Under Test (SUT) still works as expected after changes. The simplest regression testing approach is Retest-all, which consists of re-executing the entire Test Suite (TS) on the new version of the SUT. When SUT and its TS grow in size, applying Retest-all could be expensive. Test Suite Reduction (TSR) approaches would allow overcoming the above-mentioned issues by reducing TSs while preserving their fault-detection capability. Aim. In this paper, we introduce GASSER (Genetic Algorithm for teSt SuitE Reduction), a new approach for TSR based on a multiobjective evolutionary algorithm, namely NSGA-II. Method. GASSER reduces TSs by maximizing statement coverage and diversity of test cases, and by minimizing the size of the reduced TSs. Results. The preliminary study shows that GASSER reduces more the TS size with a small effect on fault-detection capability when compared with traditional approaches. Conclusions. These outcomes highlight the potential benefits of the use of multi-objective evolutionary algorithm in TSR field and pose the basis for future work.