A comparison study of binary multi-objective Particle Swarm Optimization approaches for test case selection

During the software testing process many test suites can be generated in order to evaluate and assure the quality of the products. In some cases the execution of all suites cannot fit the available resources (time, people, etc). Hence, automatic Test Case (TC) selection could be used to reduce the suites based on some selection criterion. This process can be treated as an optimization problem, aiming to find a subset of TCs which optimizes one or more objective functions (i.e., selection criteria). The majority of search-based works focus on single-objective selection. In this light, we developed mechanisms for functional TC selection which considers two objectives simultaneously: maximize requirements coverage while minimizing cost in terms of TC execution effort. These mechanisms were implemented by deploying multi-objective techniques based on Particle Swarm Optimization (PSO). Due to the drawbacks of original binary version of PSO we implemented five binary PSO algorithms and combined them with a multi-objective versions of PSO in order to create new optimization strategies applied to TC selection. The experiments were performed on two real test suites, revealing the feasibility of the proposed strategies and the differences among them.