| 回环软件缺陷数量预测模型 |
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| 引用本文： | 李莉,纪欣沅,宋嵩. 回环软件缺陷数量预测模型[J]. 计算机工程与应用, 2021, 57(7): 158-163. DOI: 10.3778/j.issn.1002-8331.1912-0452 |
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| 作者姓名： | 李莉 纪欣沅 宋嵩 |
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| 作者单位： | 东北林业大学 信息与计算机工程学院,哈尔滨 150040 |
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| 摘 要： | 软件缺陷预测是软件工程中的一个研究热点问题,通常软件缺陷预测的研究工作主要关注于软件模块是否存在缺陷和软件模块存在缺陷的数量.目前软件缺陷数量研究主要集中在基于缺陷数的软件模块排序.为提高软件模块排序的准确度,提出一种回环软件缺陷数量预测模型.此模型主要包括回环特征选择和缺陷预测两部分.在回环特征选择部分,将改进的密度...
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| 关 键 词： | 软件缺陷数量预测 密度峰值聚类 回环特征选择 反距离加权法 集成学习 |
Prediction Model for Number of Software Defects in Loop |
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| LI Li,JI Xinyuan,SONG Song. Prediction Model for Number of Software Defects in Loop[J]. Computer Engineering and Applications, 2021, 57(7): 158-163. DOI: 10.3778/j.issn.1002-8331.1912-0452 |
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| Authors: | LI Li JI Xinyuan SONG Song |
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| Affiliation: | College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China |
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| Abstract: | Software defect prediction is a hot research topic in software engineering. Generally, the research work of software defect prediction mainly focuses on whether there are defects in software modules and the number of defects in software modules. At present, the research of software defect quantity mainly focuses on software module sequencing based on defect quantity. In order to improve the accuracy of software module sorting, this paper proposes a prediction model of loopback software defects. This model includes two parts：loop feature selection and defect prediction. In the loop feature selection part, the improved density peak clustering algorithm and the wrapped feature selection method are combined to dynamically select the optimal feature in the loop way and train the learner. In the defect prediction part, the inverse distance weighted integration method is used to get the prediction results. The experimental results show that compared with LRCR, GRCR, LR, MLP, GP, NBR and ZIP, the model is improved by 10.36%, 28.74%, 13.51%, 36.61%, 25.30%, 60.14% and 54.72% respectively, it is helpful to improve the efficiency of software testing. |
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| Keywords: | software defect quantity measurement density peak clustering loop feature selection inverse distance weighting method integrated learning |
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