ABSTRACT Traditional way of optimizing regression testing cost is to reduce subsets of test cases from a test suite without compromising the test requirement. In order to reduce the test suite, researchers have presented various test-suite reduction techniques using coverage metrics and greedy search algorithms. Besides greedy algorithms, optimization-based algorithms have played a major role in test suite reduction. Accordingly, we developed a new optimization algorithm called, TBAT algorithm to handle the diversity problem in generating new solutions while finding the optimal test cases. Here, a fitness function is developed to select the test cases optimally through the TBAT algorithm using two constraints, satisfying the entire test requirement and minimizing the cost measure. The proposed TBAT algorithm is experimented with five programs from SIR using four different evaluation metrics. The empirical study on the performance of the TBAT algorithm is analyzed with various parameters and the comparison is done with the greedy–based algorithm and the Systolic Genetic Search (SGS) algorithm. The experimental outcome showed that the proposed TBAT algorithm outperformed the existing algorithm in reaching the minimal cost requirements.
