An Improved Artificial Bee Colony Optimization Algorithm for Test Suite Minimization

testing is essential process for maintaining the quality of software. Due to changes in customer demands or industry, software needs to be updated regularly. Therefore software becomes more complex and test suite size also increases exponentially. As a result, testing incurs a large overhead in terms of time, resources, and costs associated with testing. Additionally, handling and operating huge test suites can be cumbersome and inefficient, often resulting in duplication of effort and redundant test coverage. Test suite minimization strategy can help in resolving this issue. Test suite reduction is an efficient method for increasing the overall efficacy of a test suite and removing obsolete test cases. The paper demonstrates an improved artificial bee colony optimization algorithm for test suite minimization. The exploitation behavior of algorithm is improved by amalgamating the teaching learning based optimization technique. Second, the learner performance factor is used to explore the more solutions. The aim of the algorithm is to remove the redundant test cases, while still ensuring effectiveness of fault detection capability. The algorithm compared against three established methods (GA, ABC, and TLBO) using a benchmark dataset. The experiment results show that proposed algorithm reduction rate more than 50% with negligible loss in fault detection capability. The results obtained through empirical analysis show that the suggested algorithm has surpassed the other algorithms in performance.