Solving test suite reduction problem using greedy and genetic algorithms

Abstract

Regression testing is an important process for software quality. Test case reduction is one of the widely used techniques for regression testing, which can dramatically decrease the testing costs. However, it is an NP-complete problem and big test cases cannot be accomplished in reasonable amount of time. For this reason, we propose a test suite reduction approach by using greedy and genetic algorithms. The greedy algorithm found a wide usage in previous studies thanks to its simplicity, but we already know that it sticks to local optima and does not benefit from metaheuristics. Thus, we used genetic algorithm to overcome its weaknesses. Our experimental results prove that metaheuristics and evolutionary algorithms perform better than the greedy approaches.