Application of the Law of Minimum and Dissimilarity Analysis to Regression Test Case Prioritization

Abstract

Regression testing is one of the most expensive processes in testing. Prioritizing test cases in regression testing is critical for the goal of detecting the faults sooner within a large set of test cases. We propose a test case prioritization (TCP) technique for regression testing called LoM-Score inspired by the Law of Minimum (LoM) from biology. This technique calculates the impact probabilities of methods calculated by change impact analysis with forward slicing and orders test cases according to LoM. However, this ordering doesn't consider the possibility that consecutive test cases may be covering the same methods repeatedly. Thereby, such ordering can delay the time of revealing faults that exist in other methods.To solve this problem, we enhance the LoM-Score TCP technique with an adaptive approach, namely with a dissimilarity-based coordinate analysis approach. The dissimilarity-based coordinate analysis uses Jaccard Similarity for calculating the similarity coefficients between test cases in terms of covered methods and the enhanced technique called Dissimilarity-LoM-Score (Dis-LoM-Score) applies a penalty with respective on the ordered test cases. We performed our case study on 10 open-source Java projects from Defects4J, which is a dataset of real bugs and an infrastructure for controlled experiments provided for software engineering researchers. Then, we hand-seeded multiple mutants generated by Major, which is a mutation testing tool. Then we compared our TCP techniques LoM-Score and Dis-LoM-Score with the four traditional TCP techniques based on their Average Percentage of Faults Detected (APFD) results.INDEX TERMS Change impact analysis, regression testing, software testing, test case prioritization.

The Hybrid Jaro-Winkler and Manhattan Distance using Dissimilarity Measure for Test Case Prioritization Approach

“…However, dissimilarity-based prioritization (DBP) considers the diversity among test cases. DBP aims to select a diverse set of test cases that covers different aspects of the software under test [23,24]. By considering dissimilarities between test cases, this approach ensures comprehensive coverage and reduces the risk of missing critical defects [25].…”