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.

翻译：软件缺陷预测是软件质量保证的关键环节，它能够实现缺陷的早期识别与缓解，从而降低软件故障的成本与影响。近年来，量子计算已成为一项有望变革多个领域的突破性技术；量子机器学习（QML）正是其中之一。QML算法利用量子计算的能力，以比经典算法更高的效率与效能解决复杂问题。然而，其在软件工程领域应用于软件缺陷预测的研究仍有待探索。本研究通过比较三种QML算法与五种经典机器学习（CML）算法在20个软件缺陷数据集上的性能，致力于填补这一研究空白。我们的研究呈现了QML与CML算法的对比场景，并识别出在预测软件缺陷方面表现更优且更稳定的算法。基于本次研究实践中获得的经验，我们还重点探讨了在实际软件缺陷数据集中应用QML算法所面临的挑战与未来方向。本研究的发现有助于从业者和研究人员在此领域进一步推进，从而构建更可靠、无缺陷的软件系统。