Error detection framework for complex software systems
EWDC '11 Proceedings of the 13th European Workshop on Dependable Computing
OS-level hang detection in complex software systems
International Journal of Critical Computer-Based Systems
Operating system support to detect application hangs
VECoS'08 Proceedings of the Second international conference on Verification and Evaluation of Computer and Communication Systems
A Recovery-Oriented Approach for Software Fault Diagnosis in Complex Critical Systems
International Journal of Adaptive, Resilient and Autonomic Systems
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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.