期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

偏相关方法在软件缺陷预测中的应用 被引量：3

Partial correlation analysis for software defect prediction
在线阅读 下载PDF
导出
摘要 为了提高预测模型的性能,解决不同属性子集带来的分歧,提出了基本偏相关方法的预测模型。首先,该方法在公开数据集上分析出代码静态属性与缺陷数之间存在偏相关关系;然后基于偏相关系数值,计算出代码复杂性度密度属性值;最后基于该属性值建立新的缺陷预测模型。实验表明,该模型具有较高的召回率和很好的F-measure性能,从而进一步证实了代码属性与模块缺陷之间的偏相关性是影响软件质量预测性能的重要因素的结论。该结论有助于建立更加稳定可靠的软件缺陷预测模型。 In order to improve the performance of predictors,and reduce the dissention,this paper propsed a new predict model based on partial correlation analysis.Firstly,different to prior works,analyzed the correlation between attributes and defects.Then computed code complexity density values.Based on these values,built a new predictor.Experiments were performed on the public Eclipse dataset.This predictor had a good performance with high recall rates and substantially high F-measure values.The satisfactory results also confirm the partial correlation is a very important factor in software quality analysis.This conclusion is helpful for building more stable defect predictors.
作者 马樱 罗光春 李炯 陈爱国
机构地区 电子科技大学计算机科学与工程学院
出处 《计算机应用研究》 CSCD 北大核心 2012年第2期594-596,613,共4页 Application Research of Computers
基金 新世纪优秀人才支持计划资助项目(NCET-10-0298) 四川省科技支撑计划资助项目(2011GZ0192) 中央高校基本科研业务费专项资助项目(ZYGX2009J066)
关键词 软件缺陷预测 代码静态属性 实证 复杂度 机器学习 偏相关 software defect prediction static code attributes empirical complexity machine learning partial correlation
分类号 TP301.5 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献17

  • 1CATAL C. Software fault prediction: a literature review and current trends[ J]. Expert Systems with Applications: An International Journal,2011,38(4) :4626-4636.
  • 2LIU Yi, KHOSHGOFTAAR T M, SELIYA N. Evolutionary optimiza- tion of software quality modeling with multiple repositories[ J]. IEEE Trans on Software Enclineering,2010,36(6) :852-864.
  • 3MENZIES T, MILTON Z, TURHAN B, et al. Defect prediction from static code features : current results, limitations, new approaches [ J ]. Automated Software Engineering ,2010,17 (4) :375-407.
  • 4AUBRAMANYAN R, KRISHNAN M S. Empirical analysis of CK metrics for object-oriented design complexity : implications for software defects[ J ]. IEEE Trans on Software Engineering,2003,29 (4) : 297-310.
  • 5GYIMOTHY T, RERENC R, SIKET I. Empirical validation of ob- ject-oriented metrics on open source software for fault prediction [ J ]. IEEE Trans on Software Engineering ,2005,31 (10) :897-910.
  • 6KHOSHGOFTAAR T M, SELIYA N. The necessity of assuring quali- ty in software measurement data[ C ]// Proc of the 10th International Conference on Software Metrics. 2004 : 119-130.
  • 7MENZIES T, GREENWALD J, FRANK A. Data mining static code attributes to learn defect predictors [ J ]. IEEE Trans on Software Engineering ,2007,32( 11 ) :2-13.
  • 8SHEPPED M, INCE D. A critique of three metrics[ J]. Journal of Systems and Software, 1994,26 ( 3 ) : 197- 210.
  • 9FENTON N, OHLSSON N. Quantitative analysis of faults and failures in a complex software system[J]. IEEE Trans on Software Engi- neering, 2000,26 ( 8 ) :797- 814.
  • 10ANDERSSON C, RUNESON P. A replicated quantitative analysis of fault distributions in complex software systems[ J]. IEEE Trans on Software Engineering,2007,33 ( 5 ) :273- 286.

二级参考文献58

  • 1Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In Proc. the 11th Annual Conference on Computational Learning Theory, Madison, USA, Jul.24-26,1998,pp.92-100.
  • 2Goldman S, Zhou Y. Enhancing supervised learning with un-labeled data. In Proc. the 17th International Conference onMachine Learning, San Francisco, USA, Jun. 29-Jul.2,2000,pp.327-334.
  • 3Li M, Zhou Z H. Improve computer-aided diagnosis with ma-chine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans,2007,37(6):1088-1098.
  • 4Zhou Z H, Li M. Tri-training: Exploiting unlabeled data us-ing three classifiers. IEEE Transactions on Knowledge and Data Engineering,2005,17(11):1529-1541.
  • 5Zhou Z H, Li M. Semi-supervised regression with co-training style algorithms. IEEE Transactions on Knowledge and Data Engineering,2007,19(11):1479-1493.
  • 6Steedman M, Osborne M, Sarkar A et al. Bootstrapping sta-tistical parsers from small data sets. In Proc. the 11th Con-ference on the European Chapter of the Association for Com-putational Linguistics, Budapest, Hungary, Apr.12-17,2003, pp.331-338.
  • 7Li M, Zhou Z H. Semi-supervised document retrieval. Infor-mation Processing & Management,2009,45(3):341-355.
  • 8Zhou Z H, Chen K J, Dai H B. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems,2006,24(2):219-244.
  • 9Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P SMOTE: Synthetic minority over-sampling technique. Jour-nal ol Artificial Intelliqence Research,2002,16:321-357.
  • 10Kubat M, Matwin S. Addressing the curse of imbalanced training sets: One-sided selection. In Proe. the 14th Int. Conf. Machine Learning, Nashville, USA,1997,pp.179-186.

共引文献179

同被引文献26

引证文献3

二级引证文献32

计算机应用研究
2012年 第2期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部