| Abstrakt: |
Software testing is very labor-intensive and expensive process. It is a core activity in quality assurance. This paper presents a comprehensive review of the work done in soft computing techniques to solve software test cases optimization problem. Test cases minimization, selection, prioritization and filtration are related by a common thread of test cases optimization. Test case optimization is a multi-objective optimization, peculiar nature and NP-Complete problem. Testing efforts can be reduced by applying more appropriate test case optimization techniques. Automation of testing process and multi-objective test cases optimization will help in improving the overall quality of the software. Present paper gives the insight in 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. This paper summarizes the various proposals related to test cases optimization. In addition to this, it highlights some research issues relating to above. Gaps in present study may suggest and guide the direction of future research. [ABSTRACT FROM AUTHOR] |
