Test Suite Reduction via Evolutionary Clustering

Abstract

Test suite reduction is an effective way to reduce the cost of regression testing by identifying and removing redundant test cases from the original test suite. In this paper, we propose a novel cluster test suite reduction using an evolutionary multi-objective optimization algorithm. Specifically, we use a K-means algorithm to group similar test cases to the same cluster. Then the evolutionary algorithm is used to remove redundant test cases based on the clustering results, and optimization objects are represented as the coverage-related criteria, fault-related criteria and cost-related criteria. The experimental results involving eight subject programs show that the proposed method can outperform the other three state-of-the-arts with respect to both fault detection (4.61% -9.44%) and reduction ratio (4.10% -10.64%). Meanwhile, the experiments also prove that our method has a better performance of missing failure rate (0.049% -0.132%) and code coverage rate (3.34% -6.10%). Besides, the proposed method costs are found to be comparable to the other techniques.

Keywords

• Regression testing
• cluster analysis
• genetic algorithm
• test suite reduction