A Constrained Particle Swarm Optimization Approach for Test Case Selection.
Luciano S. de Souza, Ricardo B. C. Prudêncio, Flávia de Almeida Barros · Software Engineering and Knowledge Engineering · 2010
Automatic Test Case selection is an important task to improve the efficiency of Software Testing. This task is commonly treated as an optimization problem, whose aim is to find a subset of test cases (TC) which maximizes the coverage of the software requirements. In this work, we propose the use of Particle Swarm Optimization (PSO) to treat the problem of TC selection. PSO is a promising optimization approach which was not yet investigated for this problem. In our work, PSO was used not only to maximize coverage of requirements, but also to consider the cost (execution effort) of the selected TCs. For this, we developed a constrained PSO algorithm in which execution effort was treated as a constraint in the search, whereas the requirements coverage is used as the fitness function. We highlight that, although execution effort is an important aspect in the test process, it is generally neglected by the previous work that adopted search techniques for TC selection. In experiments performed in different test suites, PSO obtained good results when compared to other search techniques.