Identifying effective test cases through K-means clustering for enhancing regression testing

Testing is the most time consuming and expensive process in the software development life cycle. In order to reduce the cost of regression testing, we propose a test case classification methodology based on k-means clustering with the purpose of classifying test cases into two groups of effective and non-effective test cases. The clustering strategy is based on Hamming distances measured over the differences between coverage information obtained for current and the previous releases of the program under test. Our empirical study shows that the clustering-based test case classification can 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 various factors including coverage criteria and the weights factor used in measuring distances.