Published Paper Details:

Abstract : Software testing has been established that testing, analysis and debugging prices typically consume over five hundredth of the prices related to the event of huge software systems. Several researchers have found many approaches to schedule associate order for execution. They incorrectly schedule tests and also the value is overrun throughout the prioritization method. To improve the regression testing process, test case prioritization techniques systematizes the execution level of test cases. One of the most critical activities of software development and maintenance, known as regression testing. Regression testing has been proved to be crucial stage of software testing. In our proposed study we introduce a hybrid firefly algorithm based regression test case prioritization. The major contribution of our study is regression test case generation, factors identification, clustering for test case prioritization, optimization of prioritized test case. In this research, the K-means clustering algorithm will be used to separate the relevant test cases from irrelevant test cases. Relevant test cases denote the prioritized test cases. We will consider only this relevant test cases resultant from the clustering algorithm in order to optimize it with Hybrid Fire Fly Algorithm (HFFA). HFFA works by the hybridization of Artificial Bee Colony (ABC) Algorithm and Fire Fly (FF) Algorithm. ABC algorithm is a part of a swarm based meta-heuristic algorithm which is enthused by the sharp foraging behavior of the honey bees. In our HFFA Optimization Algorithm, the FF will be processed within the scout bee component of ABC, which leads to fast convergence and limited search space controlled based optimization of locations. Thus we will obtain Effective prioritized test cases.