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论文:2015,Vol:33,Issue(2):332-336 |
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引用本文： |
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郑炜, 吴潇雪, 谭鑫, 彭耀鹏, 杨帅. 基于半监督学习方法的软件故障定位研究[J]. 西北工业大学学报 |
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Zheng Wei, Wu Xiaoxue, Tan Xin, Peng Yaopeng, Yang Shuai. Software Fault Localization Using Semi-Supervised Learning[J]. Northwestern polytechnical university |
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| 基于半监督学习方法的软件故障定位研究 |
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| 郑炜, 吴潇雪, 谭鑫, 彭耀鹏, 杨帅 |
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| 西北工业大学软件与微电子学院, 陕西西安 710072 |
| 摘要: |
| 故障定位是软件工程中最为耗时和昂贵的活动之一,为降低软件故障定位的成本及提高故障定位的效率,机器学习方法被广泛应用于自动化软件故障定位中。传统的监督学习方法需要获取大量标记样本,这在实际项目中相当困难,且费用高昂。针对这一问题,提出采用半监督学习方法进行软件故障定位的思想,故障定位基于语句级别,通过应用程序中可执行语句与测试用例执行之间动态属性、以及传统软件故障定位中较有效的若干静态属性实现协同训练目的,得到训练良好的分类器,然后用该分类器对程序其余语句进行分类,从而得到故障语句。文章最后在Siemens Suite数据集中对算法进行验证,通过与传统监督学习算法进行对比,证明半监督学习算法在软件故障定位中的有效性。 |
| 关键词:
软件故障定位
半监督学习
协同训练算法
训练样本
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| Software Fault Localization Using Semi-Supervised Learning |
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| Zheng Wei, Wu Xiaoxue, Tan Xin, Peng Yaopeng, Yang Shuai |
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| Department of Software and Microelectronic Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| In order to improve the efficiency of software fault localization, supervised learning methods are widely used in automatic software fault localization. But these methods mostly ignore a very important fact:in order to train a good performance of the classifier through supervised learning method, there must be a large number of labeled samples. While in the actual project, to obtain a large number of labeled samples is quite difficult; even if it can be done, the cost is very high. In order to solve this problem, we propose a semi supervised learning algorithm for software fault location. We adopt a high-creditability and collaborative style of semi supervised learning algorithm named Co-Trade, which uses the dynamic attributes between the programs' executable statements and test case execution as well as some effective static attributes of traditional software fault localization to achieve the purpose of cooperative training. Finally, selecting the Siemens Suite as the test data, we prove the validity of Co-Trade algorithm in software fault localization by comparing it with the traditional supervised learning methods. |
| Key words:
backpropagation algorithms
classification (of information)
classifiers
cost reduction
decision trees
errors
fault detection
MATLAB
software engineering
software testing
support vector machines
Co-Trade
dynamic attributes between the programs' executable statements and test case execution
semi-supervised learning
software fault localization
training data
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| 收稿日期: 2014-10-28
修回日期:
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| DOI: |
| 通讯作者:
Email： |
| 作者简介: 郑炜(1975-),西北工业大学副教授,主要从事软件工程、软件测试的研究。
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| 相关功能 |
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| 作者相关文章 |
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| 郑炜 在本刊中的所有文章 |
| 吴潇雪 在本刊中的所有文章 |
| 谭鑫 在本刊中的所有文章 |
| 彭耀鹏 在本刊中的所有文章 |
| 杨帅 在本刊中的所有文章 |
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参考文献: |
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[1] Binkley D. Source Code Analysis:a Road Map[C] //Proceedings of Future of Software Engineering, Minneapolis, USA, 2007:104-119
[2] Zhang M L, Zhou Z H. CoTrade:Confident Co-Training with Data Editing[J]. IEEE Trans on Systems, Man, and Cybernetics, Part B:Cybernetics, 2011, 41(6):1612-1626
[3] Ali S, Andrews J H, Dhandapani T, et al. Evaluating the Accuracy of Fault Localization Techniques[C] //Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering, 2009:76-87
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[5] Wong W E, Qi Y. BP Neural Network-Based Effective Fault Localization[J]. International Journal of Software Engineering and Knowl6edge Engineering, 2009, 19(4):573-597
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[7] Jones J A, Harrold M J, Stasko J. Visualization of Test Information to Assist Fault Localization[C] //Proceedings of the 24th International Conference on Software Engineering, 2002:467-477 |
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