Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

Abstract Software defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.