Efficient Test Case Reduction Using Grey Wolf Optimization Techniques

Software testing is a crucial stage in the software development life cycle that ensures the reliability and accuracy of applications. But maintaining large test suites is very expensive. Test suite reduction addresses this concern by eliminating duplicate test cases while preserving the ability to detect mistakes. Motivated by the hunting and leadership of grey wolves, we propose a novel approach to test suite reduction called Grey Wolf Optimization (GWO) based test suite reduction in the present work. We formalize the test suite reduction problem into an optimization problem, which tries to preserve the useful test cases and prune the redundancy. GWO efficiently searches over the space, exploring/exploiting tradeoff to find the best subset of test cases. Our experimental evaluation on popular benchmark datasets indicates that, compared with typical reduction techniques, the proposed approach largely reduces test-suite size and keeps or improves fault detection capabilities. By reducing execution time and resource utilization, while maintaining the test coverage, this approach improves the effectiveness of the software testing process. The results are indicative of the promise of bio-inspired algorithms in software engineering and hold promise for future development of TSOS techniques.