A Clustering-Based Test Case Classification Technique for Enhancing Regression Testing

Abstract

To reduce the cost of regression testing, we propose a test case classification methodology based on clustering techniques to classify test cases into effective and non-effective groups. The clustering strategy is based on the coverage information obtained for the earlier releases of the program under test. We employed two common clustering algorithms namely centroid-based and hierarchical clustering. The empirical study results showed the test case clustering can effectively identify effective test cases with high recall ratio and considerable accuracy percentage. The paper also investigates and compares the performance of the proposed clustering-based approach with some other factors including coverage criteria, construction of features, and quantity of faults in the earlier releases.