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Lightweight Fault Diagnosis of Software
{ Wed, 15 Jun 2011, 14h00 }

By: Rui Maranhão  [ show info ]

Automatic fault localization techniques aid developers/testers to pinpoint the root cause of software faults, thereby reducing the debugging effort. Depending on the amount of knowledge that is required about the system’s internal component structure and behavior, current, predominant approaches to automatic software fault localization can be classified as (1) statistics-based approaches, and (2) reasoning approaches. Statistics-based fault localization techniques such as SFL use program spectra to find a statistical relationship with observed failures. While modeling costs and computational complexity are minimal, SFL’s diagnostic accuracy is inherently limited as no reasoning is used. In contrast to SFL, model-based reasoning approaches use prior knowledge of the system, such as component interconnection and statement semantics, to build a model of the correct behavior of the system. While delivering higher diagnostic accuracy, they suffer from high computation complexity. In this talk, we present a novel, low-cost, Bayesian reasoning approach to spectrum-based multiple fault localization, coined Barinel. A central feature of our contribution is the use of a generic, intermittent component failure model. Whereas previous approaches have used approximations instead, in Barinel component intermittency rate is computed as part of the posterior candidate probability computation, using a maximum likelihood estimation procedure. This procedure optimally exploits all information contained by the program spectra. Our synthetic and real software experiments (not only including well-known benchmark programs, but also experiments on the Philips TV software) show that Barinel outperforms other state-of-the-art approaches.


Hosted by: Software Systems

Location: DI seminars room

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