Multi-Objective Test Case Selection: A study of the influence of the Catfish effect on PSO based strategies

Abstract

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 can not 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). In this light, we developed mechanisms for TC selection in context of structural and functional testing. The proposed algorithms consider two objectives simultaneously: maximize branch coverage (or functional requirements coverage) while minimizing execution cost (time). These mechanisms were implemented by deploying multi-objective techniques based on Particle Swarm Optimization (PSO). Additionally, we added the so-called catfish effect into the multi-objective selection algorithms in order to improve their results. The performed experiments revealed the feasibility of the proposed strategies.