Software development involves structured methodologies encompassing coding, testing, debugging, and deployment, but faces challenges due to large codebases, diverse contributors, and high complexity. Manual testing and defect detection are increasingly inadequate, especially given the test oracle problem and coverage gaps. Artificial intelligence (AI) offers a promising alternative, enabling automated test case generation and defect identification. However, AI-based models struggle with imbalanced datasets, unstable software metrics, and the complexity of hyperparameter tuning—an NP-hard problem. To address this, metaheuristic algorithms, galvanized by nature, provide efficient search strategies for optimization. This study contributes by developing a modified metaheuristic algorithm for hyperparameter optimization, presenting a classifier for software defect detection, and proposing a supporting framework for continuous development and tuning. Real world testing using publicity available data suggest promising outcomes, with the top performing models tuned with the adapted algorithm attaining accuracy as high as .903846.