메트릭에 따른 탐색 기반 테스팅 알고리즘 비교

Abstract

Search-Based Software Testing (SBST) is an effective technique for test data generation on large domain size. Although the performance of SBST seems to be affected by the structural characteristics of Software Under Test (SUT), studies for the comparison of SBST techniques considering structural characteristics are rare. In addition to the comparison study for SBST, we analyzed the best algorithm with different structural characteristics of SUT. For the generalization of experimental results, we automatically generated 19,800 SUTs by combining four metrics, which are expected to affect the performance of SBST. According to the experiment results, Genetic algorithm showed the best performance for SUTs with high complexity and test data evaluation with count . On the other hand, the genetic simulated annealing and the simulated annealing showed relatively better performance for SUTs with high complexity and test data evaluation with count . Genetic simulated annealing, simulated annealing and hill climbing showed better performance for SUTs with low complexity.