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| 软件缺陷预测技术研究进展 |
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宫丽娜1,2,3, 姜淑娟1,2, 姜丽1,2
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1.中国矿业大学 计算机科学与技术学院, 江苏 徐州 221116;2.矿山数字化教育部工程研究中心, 江苏 徐州 221116;3.枣庄学院 信息科学与工程学院, 山东 枣庄 277160
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| 摘要: |
| 随着软件规模的扩大和复杂度的不断提高，软件的质量问题成为关注的焦点，软件缺陷是软件质量的对立面，威胁着软件质量，如何在软件开发的早期挖掘出缺陷模块成为一个亟需解决的问题.软件缺陷预测通过挖掘软件历史仓库，设计出与缺陷相关的内在度量元，然后借助机器学习等方法来提前发现与锁定缺陷模块，从而合理地分配有限的资源.因此，软件缺陷预测是软件质量保证的重要途径之一，近年来已成为软件工程中一个非常重要的研究课题.汇总近8年（2010年~2017年）国内外的缺陷预测技术的研究成果，并以缺陷预测的形式为主线进行分析，首先介绍了软件缺陷预测模型的框架；然后从软件缺陷数据集、构建模型的方法及评价指标这3个方面对已有的研究工作进行分类归纳和比较；最后探讨了软件缺陷预测的未来可能的研究方向、机遇和挑战. |
| 关键词: 软件缺陷预测 软件度量 数据预处理 机器学习 性能评价指标 |
| DOI：10.13328/j.cnki.jos.005790 |
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| 基金项目:国家自然科学基金（61673384） |
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| Research Progress of Software Defect Prediction |
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GONG Li-Na1,2,3, JIANG Shu-Juan1,2, JIANG Li1,2
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1.School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;2.Engineering Research Center of Mine Digitalization of Ministry of Education, Xuzhou 221116, China;3.College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China
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| Abstract: |
| With the improvement of the scale and complexity of software, software quality problems become the focus of attention. Software defect is the opposite of software quality, threatening the software quality. How to dig up defect modules in the early stages of software development has become a urgent problem that needs to be solved. Software defect prediction (SDP) designs the internal metrics related defects by mining software history repositories, and then in advance finds and locks the defect modules with the aid of machine learning methods, so as to allocate the limited resources reasonably. Therefore, SDP is one of the important ways of software quality assurance (SQA), which has become a very important research subject in software engineering in recent years. Based on the form of defect perfection, this research offers a systematic analysis of the existing research achievements of the domestic and foreign researchers in recent eight years (2010~2017). First, the research framework of SDP is given.Then the existing research achievements are classified and compared from three aspects, including datasets of SDP, the methods models and the evaluation indicators. Finally, the possible research directions are pointed out. |
| Key words: software defect prediction (SDP) software metrics data preprocessing machine learning performance evaluation criteria |
