Time and resources are usually neglected areas in the life cycle of software development. So, these become the primary constraints in software testing. Optimization of a test suite is quite crucial in reducing the complexity of the testing phase and selection of the test cases by eliminating redundant data; this is critical for defining the strategies. Most of the work in literature employs single-objective optimization methods. Though these are not always efficient, these play a critical role in the selection of a test case. Test case selection is, however, non-deterministic. Selection of test cases using Parallel Programming is treated as a complex task due to the need for higher performance in Parallel Computing. Parallel Computing can be stated as a combination of Computational mechanisms and Mathematical techniques. Hence, this investigation proposes a novel BAT algorithm for multi-objective optimization. It has code coverage as well as Object-oriented testing strategies. Comparing the experimental results with the Genetic Algorithm (GA), it is observed that the proposed method has faster convergence with adequate code coverage.
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