An Hybrid Binary Multi-Objective Particle Swarm Optimization with Local Search for Test Case Selection

During the software testing process a variety of test suites can be generated in order to evaluate and assure the quality of the products. However, in some contexts the execution of all suites does not fit the available resources (time, people, etc). In such cases, the suites could be automatically reduced based on some selection criterion. 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). In this light, we developed two new mechanisms for TC selection which consider two objectives simultaneously: maximize branch coverage while minimizing execution cost (time). These mechanisms were implemented using multi-objective techniques based on Particle Swarm Optimization (PSO). Additionally, we create hybrid multi-objective selection algorithms in order to improve the results. The experiments were performed on the space program from the SIR repository, attesting the feasibility of the proposed hybrid strategies.