Fei Qi, Yin Guisheng, Sun Zhian
College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
Jiangsu Automation Research Institute, Lianyungang, China.
PeerJ Comput Sci. 2025 Apr 16;11:e2815. doi: 10.7717/peerj-cs.2815. eCollection 2025.
Software defect detection is a critical research topic in the field of software engineering, aiming to identify potential defects during the development process to improve software quality and reduce maintenance costs. This study proposes a novel feature selection and defect prediction classification algorithm based on a multi-strategy enhanced Parrot Optimization (PO) algorithm. Firstly, to address the limitations of the original Parrot Optimization algorithm, such as strong dependence on the initial population, premature convergence, and insufficient global search capability, this article develops a multi-strategy enhanced Parrot Optimization algorithm (MEPO). Experiments conducted on eight benchmark test functions validate the superior performance of MEPO in terms of convergence speed and solution accuracy. Secondly, to mitigate the adverse impact of irrelevant features on model performance in traditional software defect prediction methods, this study introduces a binary multi-strategy enhanced Parrot Optimization algorithm (BMEPO) for optimizing feature selection. Comparative experiments demonstrate that BMEPO exhibits stronger competitiveness in feature selection quality and classification performance compared to advanced feature selection algorithms. Finally, to further enhance the classification performance of defect prediction, a heterogeneous data stacking ensemble learning algorithm (HEDSE) based on feature selection is proposed. Experimental evaluations on 16 open-source software defect datasets indicate that the proposed HEDSE outperforms existing methods, providing a novel and effective solution for software defect prediction. The proposed approaches in this study hold significant practical value, particularly in improving software quality, optimizing testing resource allocation, and reducing maintenance costs, offering broad potential for application in real-world software engineering scenarios.
软件缺陷检测是软件工程领域的一个关键研究课题，旨在在开发过程中识别潜在缺陷，以提高软件质量并降低维护成本。本研究提出了一种基于多策略增强鹦鹉优化（PO）算法的新型特征选择和缺陷预测分类算法。首先，为了解决原始鹦鹉优化算法的局限性，如对初始种群的强烈依赖、早熟收敛和全局搜索能力不足等问题，本文开发了一种多策略增强鹦鹉优化算法（MEPO）。在八个基准测试函数上进行的实验验证了MEPO在收敛速度和求解精度方面的优越性能。其次，为了减轻传统软件缺陷预测方法中无关特征对模型性能的不利影响，本研究引入了一种二进制多策略增强鹦鹉优化算法（BMEPO）用于优化特征选择。对比实验表明，与先进的特征选择算法相比，BMEPO在特征选择质量和分类性能方面表现出更强的竞争力。最后，为了进一步提高缺陷预测的分类性能，提出了一种基于特征选择的异构数据堆叠集成学习算法（HEDSE）。在16个开源软件缺陷数据集上的实验评估表明，所提出的HEDSE优于现有方法，为软件缺陷预测提供了一种新颖有效的解决方案。本研究中提出的方法具有重要的实用价值，特别是在提高软件质量、优化测试资源分配和降低维护成本方面，在实际软件工程场景中具有广阔的应用潜力。
