Enhancing software defect prediction models using metaheuristics with a learning to rank approach

In today’s world, software systems are extensively used across industries like medicine, commerce, education, entertainment, and industry. The development process includes design, development, and testing, where software testing is vital for quality assurance. Manual testing is laborious and resource-heavy, especially in large projects. Researchers are investigating methods to predict software defects by analyzing source code metrics, with Machine Learning techniques showing promise. The Machine learning models typically employs three approaches to predict defects: presence, number, and density. One cutting-edge approach in such models is LTR (Learning To Rank), which considers the order and values of predictions, aiding enterprises in efficiently allocating resources to more defective components. This article aims to enhance the performance of 5 machine learning models by leveraging 5 metaheuristics to optimize their hyperparameters. The study was conducted using 28 common datasets from the literature, with the FPA metric as the evaluation criteria. The results demonstrate that the proposed method achieved a 13% increase compared to state-of-the-art models with Random Forest, achieving an average FPA of 0.84 across the datasets.