Test Case Prioritization Based on Dissimilarity Clustering Using Historical Data Analysis

Test case prioritization reorders test cases based on their fault detection capability. In regression testing when new version is released, previous versions’ test cases are also executed to cross check the desired functionality. Historical data ensures the previous fault information, which would lead the potential faults in new version. Faults are not uniformed in all software versions, where similar test cases may stack in same faults. Most of the prioritization techniques are either similar coverage based or requirements clustering, where some used historical data. However, no one incorporate dissimilarity and historical data together, which ensure the coverage of various un-uniformed faults. This paper presents a prioritization approach based on dissimilarity test case clustering using historical data analysis to detect various faults in minimum test case execution. Proposed scheme is evaluated using well established Defects4j dataset, and it has reported that dissimilarity algorithm performs better than untreated, random and similarity based prioritization.