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Data mining approaches to software fault diagnosis

Bose, RPJC and Srinivasan, SH (2005) Data mining approaches to software fault diagnosis. In: 15th International Workshop on Research Issues in Data Engineering - Stream Data Mining and Applications, APR 03-04, 2005, Tokyo.

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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.

Item Type: Conference Paper
Additional Information: Copyright 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Others
Date Deposited: 09 Jun 2010 05:17
Last Modified: 19 Sep 2010 06:00
URI: http://eprints.iisc.ernet.in/id/eprint/27351

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