Abstract
Objectives: Algorithms analogous to the natural processes have been employed to optimize the fault detection efficiency and reduce software testing time. Test case selection for software testing is the most critical and time consuming part as exhaustive software testing is not possible. Recently, a novel compound technique BCO-m-GA for test case selection has been proposed based on non-sequential combination of stratified sampling, BCO (Bee Colony Optimization) and GA (Genetic Algorithm). This paper makes a performance evaluation of the proposed BCO-m-GA technique. Methods: This paper attempts to analyse the performance of the BCO-m-GA technique for test case selection against individual non-compounded algorithms GA and BCO. Findings: The results underline the improved performance of BCO-m-GA in comparison to GA and BCO in terms of differential of number of Decision Nodes Covered (DNC) and rate of change of Fitness Score (FS) of the optimal solution. Improvements: The insights obtained in this study would help researchers in improved framing of test case selection procedures. Keywords: Bee Colony Optimization, Genetic Algorithm, Software Testing, Test Case Optimization, Test Case Selection
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