Software testing is a principal however complex piece of software development life cycle. It points towards recognizing the bugs and faults in the program of functional behavior. It always needs generation of test cases and suites for confirming their input ranges. The Optimization of Software testing is the foremost challenge. Subsequently to achieve greatest coverage the test must be created from overall dispersed regions of input areas, known Partition testing. Random testing serve better than partition testing nevertheless it also generating high computational overheads. Another technique is Adaptive Random technique having adaptive nature of recovering and finishing a portion of the test cases back to the input for correcting the next test cases lined to be passed. Adaptive Random Partition Testing (ARPT) was used to test software which utilized AT and RT in an alternative manner. The computational intricacy issue of random partitioning in ARPT strategies was resolved by utilizing clustering algorithms. It expends additional time and it prompts overhead procedure to estimate parameters of ARPT. In this paper, the parameters of ARPT 1 and ARPT 2 are optimized using Bacterial Foraging Algorithm (BFA) and Improved BAT algorithm which improves the accuracy of ARPT software testing strategies. However, the BFA has the most critical parameter step size that has strong influence in the convergence and stability of algorithm. In order to solve these problems, the improvised BAT optimization algorithm is proposed in this paper. It improves the accuracy and reduces time consumption of parameter setting of ARPT testing strategies.

Software testing, Adaptive Random Partition Testing, BFA, Improvised BA

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