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On the Accuracy of Spectrum-based Fault Localization
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
A new bayesian approach to multiple intermittent fault diagnosis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Locating software components which are responsible for observed failures is the most expensive, error-prone phase in the software development life cycle. Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important process for the development of dependable software. In this paper we present a toolset for automatic fault localization, dubbed Zoltar, which adopts a spectrum-based fault localization technique. The toolset provides the infrastructure to automatically instrument the source code of software programs to produce runtime data, which is subsequently analyzed to return a ranked list of likely faulty locations. Aimed at total automation (e.g., for runtime fault diagnosis), Zoltar has the capability of instrumenting the program under analysis with fault screeners, for automatic error detection. Using a small thread-based example program as well as a large realistic program, we show the applicability of the proposed toolset.