Software regression test case prioritization for object-oriented programs using genetic algorithm with reduced-fitness severity

One of the most important activities in software maintenance is Regression testing. The re-execution of all test cases during the regression testing is costly. And even though several of the code based addresses procedural programs. Some researchers addressed the issue of test case prioritization using Genetic Algorithm, but the authors do not select modification-revealing before prioritization and used the same fault severity. This paper propose an optimized regression test case selection and prioritization for object-oriented software based on dependence graph model analysis of the source code and optimized the selected test case using Genetic Algorithm. The goal is to select modification-revealing test cases and order them based on their fitness value computed using previous history of the fault severity. A sample test suite is used to evaluate the performance of our proposed approach. We measure the performances of our prioritization approach using Average Percentage of rate of Faults Detection (APFD) metric. This proposed approach will increase the efficiency and effectiveness of regression testing in term of rate of fault detection. GA with reduced severity of fault prioritized selected test cases more effectively compared to using GA with the same severity of fault and non-prioritize, which result in reducing the cost of regression testing.

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