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<p>Software defect prediction (SDP) is a critical task for improving software quality and reducing development costs by identifying faults early. While machine learning models, particularly XGBoost, have been widely adopted for SDP, their performance is highly dependent on optimal hyperparameter tuning. Furthermore, existing state-of-the-art methods—including deep learning-based approaches leveraging semantic code features—often suffer from high computational complexity, and extensive training requirements. To address these challenges, this paper proposes a hybrid optimization approach, RL-SWO, which integrates the Spider Wasp Optimizer (SWO) with reinforcement learning (RL) to refine XGBoost’s parameters. RL-SWO was first validated on CEC’22 benchmark functions, where it outperformed several state-of-the-art metaheuristics. It was then applied to five defect prediction datasets from the AEEEM repository, demonstrating superior performance in detecting defective and non-defective instances, particularly in imbalanced data scenarios. Compared to traditional optimization methods, RL-SWO significantly improved XGBoost’s classification accuracy and robustness. Experimental results highlight RL-SWO’s potential in enhancing SDP models by balancing exploration and exploitation during optimization. This study advances automated defect prediction by leveraging metaheuristics and reinforcement learning, offering a promising approach for improving software reliability.</p>

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Predicting software defects using an extreme gradient boosting model tuned with reinforcement learning based spider wasp optimizer

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Raja Oueslati,
  • Mohamed Wajdi Ouertani,
  • Ghaith Manita,
  • Amit Chhabra

摘要

Software defect prediction (SDP) is a critical task for improving software quality and reducing development costs by identifying faults early. While machine learning models, particularly XGBoost, have been widely adopted for SDP, their performance is highly dependent on optimal hyperparameter tuning. Furthermore, existing state-of-the-art methods—including deep learning-based approaches leveraging semantic code features—often suffer from high computational complexity, and extensive training requirements. To address these challenges, this paper proposes a hybrid optimization approach, RL-SWO, which integrates the Spider Wasp Optimizer (SWO) with reinforcement learning (RL) to refine XGBoost’s parameters. RL-SWO was first validated on CEC’22 benchmark functions, where it outperformed several state-of-the-art metaheuristics. It was then applied to five defect prediction datasets from the AEEEM repository, demonstrating superior performance in detecting defective and non-defective instances, particularly in imbalanced data scenarios. Compared to traditional optimization methods, RL-SWO significantly improved XGBoost’s classification accuracy and robustness. Experimental results highlight RL-SWO’s potential in enhancing SDP models by balancing exploration and exploitation during optimization. This study advances automated defect prediction by leveraging metaheuristics and reinforcement learning, offering a promising approach for improving software reliability.