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Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important technique for the development of dependable software. In this paper we study different similarity coefficients that are applied in the context of a program spectral approach to software fault localization (single programming mistakes). The coefficients studied are taken from the systems diagnosis/automated debugging tools Pinpoint, Tarantula, and AMPLE, and from the molecular biology domain (the Ochiai coefficient). We evaluate these coefficients on the Siemens Suite of benchmark faults, and assess their effectiveness in terms of the position of the actual fault in the probability ranking of fault candidates produced by the diagnosis technique. Our experiments indicate that the Ochiai coefficient consistently outperforms the coefficients currently used by the tools mentioned. In terms of the amount of code that needs to be inspected, this coefficient improves 5% on average over the next best technique, and up to 30% in specific cases
Abreu et al. (Sun,) studied this question.