Acceptance testing based test case prioritization

Software testing is an important and expensive phase of development. Whenever changes are made in the code, it becomes a time-consuming process to execute all the test cases in regression testing. Therefore, the testing process needs some test case reduction techniques and prioritization techniques to improve the regression testing process. Test case prioritization aims at ordering test cases to increase the fault detection capability. There are many existing techniques for test case reduction as well as for prioritization that use the coverage information which degrades the number of ties uncounted during the prioritization. This paper will take its focus on the multi-level random walk algorithm, which has been used for test case reduction. In this process, test case selection for further reduction is done randomly on every iteration that degrades the performance of a testing process in terms of coverage and will also generate a situation for random test case tie. To overcome this situation of random test case selection and handling test case tie, a solution is being proposed in this paper, which includes a combination of optimized multi-level random walk and genetic algorithm. In regression testing, another important aspect is the test case prioritization that finds fault as early as possible if test cases are prioritized properly. So, this paper introduces new prioritization techniques, which are based on fault prediction in acceptance testing. The performance of the proposed approach in terms of fault detection is evaluated with the help of many programs.