Exploiting count spectra for Bayesian fault localization

Background: Automated diagnosis of software defects can drastically increase debugging efficiency, improving reliability and time-to-market. Current, low-cost, automatic fault diagnosis techniques, such as spectrum-based fault localization (SFL), merely use information on whether a component is involved in a passed/failed run or not. However, these approaches ignore information on component execution frequency, which can improve the accuracy of the diagnostic process. Aim: In this paper, we study the impact of exploiting component execution frequency on the diagnostic quality. Method: We present a reasoning-based SFL approach, dubbed Zoltar-C, that exploits not only component involvement but also their frequency, using an approximate, Bayesian approach to compute the probabilities of the diagnostic candidates. Zoltar-C is evaluated and compared to other well-known, low-cost techniques (such as Tarantula) using a set of programs available from the Software Infrastructure Repository. Re...

• Type: Journal
• Year: 2010
• Where: PROMISE
• Authors: Rui Abreu, Alberto González-Sanchez, Arjan J. C. van Gemund
• Keywords: Component Execution Frequency, PROMISE 2010, Reasoning-based Sfl Approach, Software Engineering, Spectrum-based Fault Localization