A Multi-Objective Particle Swarm Optimization for Test Case Selection Based on Functional Requirements Coverage and Execution Effort

Although software testing is a central task in the software lifecycle, it is sometimes neglected due to its high costs. Tools to automate the testing process minor its costs, however they generate large test suites with redundant Test Cases (TC). Automatic TC Selection aims to reduce a test suite 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 a mechanism for functional TC selection which considers two objectives simultaneously: maximize requirements' coverage while minimizing cost in terms of TC execution effort. This mechanism was implemented as a multi-objective optimization process based on Particle Swarm Optimization (PSO). We implemented two multi-objective versions of PSO (BMOPSO and BMOPSO-CDR). The experiments were performed on two real test suites, revealing very satisfactory results (attesting the feasibility of the proposed approach). We highlight that execution effort is an important aspect in the testing process, and it has not been used in a multi-objective way together with requirements coverage for functional TC selection.