An Improved Artificial Bee Colony Optimization Algorithm for Test Suite Minimization

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