Optimization of Test Cases using Soft Computing Techniques: A Critical Review
نویسندگان
چکیده
Software testing is the key technology for evaluating the fault detecting capability quantitatively. Software testing is very labor-intensive and expensive process. It is a core activity in quality assurance. Test cases minimization, selection, prioritization forms common thread of optimization. Test case optimization is a multi-objective optimization, peculiar nature and NP-Complete problem. However, by applying appropriate test case optimization techniques, these efforts can be reduced considerably. Moreover, by using the multi-objective optimization of test cases with test data adequacy criteria and automation of testing process will help in improving the overall quality of the software. Present paper gives the insight into existing single objective test cases optimization techniques such as Genetic Algorithms, Ant Colony Optimization, Hybrid Genetic, Intelligent Search Agent Techniques, Particle Swan Optimization, Graph based Intelligent Techniques, Hybridization of Soft Computing techniques devised by various researchers or practionners by using single parameter like number of defect detecting capability, cost, efforts, coveragebility of requirement/ code and quality of the results. In addition to this, it highlights some research issues relating to above. Key-Words: Multi-Objective Optimization, Soft Computing Techniques, Test Cases, Test Data Adequacy
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