Exploiting DBSCAN and Combination Strategy to Prioritize the Test Suite in Regression Testing

Abstract

Test case prioritization techniques improve the fault detection rate by adjusting the execution sequence of test cases. For static black-box test case prioritization techniques, existing methods generally improve the fault detection rate by increasing the early diversity of execution sequences based on string distance differences. However, such methods have a high time overhead and are less stable. This paper proposes a novel test case prioritization method (DC-TCP) based on density-based spatial clustering of applications with noise (DBSCAN) and combination policies. By introducing a combination strategy to model the inputs to generate a mapping model, the test inputs are mapped to consistent types to improve generality. The DBSCAN method is then used to refine the classification of test cases further, and finally, the Firefly search strategy is introduced to improve the effectiveness of sequence merging. Extensive experimental results demonstrate that the proposed DC-TCP method outperforms other methods in terms of the average percentage of faults detected and exhibits advantages in terms of time efficiency when compared to several existing static black-box sorting methods.