A Meta-heuristic Test Case Prioritization Method Based on Hybrid Model
Wenjun Su, Zhao Li, Zhihui Wang, Dengxin Yang · 2020 International Conference on Computer Engineering and Application (ICCEA) · 2020
Software testing is an important and complex part of the software development life cycle. Along with version changes and defect repairs of the software system under test, regression testing is required to ensure that the modified parts have no impact on the unmodified parts. In a resource-constrained environment, it is necessary to select a more valuable test case from the test case library to execute first. However, the existing prioritization methods of test cases are still insufficient in terms of Average Percentage of Faults Detected (APFD) and time execution performance, and there is a problem of large search space. Aiming at the test case priority ranking problem, this paper proposes a meta-heuristic test case prioritization method based on a hybrid model to reduce test cost. This method first establishes a hybrid model by using the correlation between test cases and the importance of test data, and then uses an improved firefly algorithm based on the hybrid model to find an optimal test sequence. This article has carried out experiments on three benchmark test programs. The test suite is from Software-artifact Infrastructure Repository (1SIR). The experimental results show that the method proposed in this paper has better performance in terms of APFD and time execution compared with existing methods, such as Greedy, Particle Swarm Optimization (PSO) and Firefly Algorithm (FA).