Data mining approaches to software fault diagnosis

R. Bose, S. Srinivasan
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引用次数: 23

Abstract

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.
软件故障诊断的数据挖掘方法
软件故障的自动识别具有重要的现实意义。这需要描述程序执行行为的特征,并在选择的表示上使用适当的数据挖掘技术。在本文中,我们使用系统调用的顺序来描述程序的执行。解决的数据挖掘任务是学习将系统调用流映射到故障标签和自动识别故障原因。前者采用了频谱核和支持向量机,后者采用了潜在语义分析,并对包含系统调用痕迹的入侵数据集进行了验证。结果表明,内核技术与最佳可用结果一样准确,但速度要快几个数量级。我们还表明,潜在语义索引能够揭示故障特定的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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