Comparative Analysis of Efficacious Metaheuristic Technique with Genetically Modified- Flower Pollination Algorithm (GM-FPA) for Test Case Prioritization in Regression Testing

Abstract

Abstract: Regression Testing is most imperative activity of software development life cycle. Test case prioritization being one of the most adopted branch for regression testing and with the invent of nature inspired metaheuristic techniques in optimization, this study makes an attempt to augments the features of test case prioritization with nature inspired metaheuristic techniques to determine the most efficacious metaheuristic techniques from Cuckoo Searh (CS) algorithm, Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) for three different case studies. APFD metrics is used to compare the algorithms. Further the study compares the most efficacious technique with Genetically Modified- Flower Pollination Algorithm (GM-FPA) to identify the most efficient technique for regression test case prioritization.