On the use of evolutionary algorithms for test case prioritization in regression testing considering requirements dependencies

Nowadays, software systems encounter repeated modifications in order to satisfy any requirement regarding a business change. To assure that these changes do not affect systems' proper functioning, those parts affected by the changes need to be retested, minimizing the negative impact of performed modifications on another part of the software. In this research, we investigate how different optimization techniques (with various criteria) could improve the effectiveness of the testing activity, in particular the effectiveness of test case prioritization. The most efficient test schedules are identified by using either a Greedy algorithm or a Genetic Algorithm, optimizing a quality function that incorporates single or multiple criteria. Both functional requirements (with the existing dependencies between them) and non-functional requirements (i.e. quality attributes for test cases) are integrated with the quality assessment of a test order. Therefore, the conducted experiments considered various criteria combinations (faults, costs, and number of test cases), being applied to both theoretical case studies and a real-world benchmark. The conclusion of the experiments shows that the Genetic Algorithm outperforms the Greedy on all considered criteria.