对比量子与经典算法在20个软件缺陷数据集上的表现
Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities
- 用3种量子与5种经典机器学习算法比较缺陷预测效果
- 量子算法在部分数据集上表现更优但稳定性不足
- 为未来量子软件工程应用提供挑战与方向参考
软件缺陷预测是保障软件质量的关键环节，可早期发现并降低缺陷带来的成本与影响。近年来，量子计算成为变革多个领域的前沿技术，量子机器学习（QML）便是其重要应用之一。相比经典方法，QML有望以更高效率和效果解决复杂问题。然而，其在软件工程中用于缺陷预测的研究仍不充分。本研究填补该空白，对比了3种QML算法与5种经典机器学习（CML）算法在20个软件缺陷数据集上的表现。研究揭示了QML与CML在不同场景下的性能差异，识别出表现更优且稳定的算法，并基于实际经验指出了在真实软件数据上应用QML所面临的挑战与未来方向。研究结果有助于推动该领域发展，助力构建更可靠、无缺陷的软件系统。
原文摘要 · Abstract (English)
Software defect prediction is a critical aspect of software quality assurance, as it enables early identification and mitigation of defects, thereby reducing the cost and impact of software failures. Over the past few years, quantum computing has risen as an exciting technology capable of transforming multiple domains; Quantum Machine Learning (QML) is one of them. QML algorithms harness the power of quantum computing to solve complex problems with better efficiency and effectiveness than their classical counterparts. However, research into its application in software engineering to predict software defects still needs to be explored. In this study, we worked to fill the research gap by comparing the performance of three QML and five classical machine learning (CML) algorithms on the 20 software defect datasets. Our investigation reports the comparative scenarios of QML vs. CML algorithms and identifies the better-performing and consistent algorithms to predict software defects. We also highlight the challenges and future directions of employing QML algorithms in real software defect datasets based on the experience we faced while performing this investigation. The findings of this study can help practitioners and researchers further progress in this research domain by making software systems reliable and bug-free.
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