Lightweight Techniques for Automatic Software Fault Localization

Current approaches to automatic software fault localization can be classified as either (1) statistics-based approaches, or (2) reasoning approaches. This distinction is based on the required amount of knowledge about the program's internal component structure and behavior. Statistics-based fault localization techniques such as Spectrum-based Fault Localization (SFL) use abstraction of program traces (also known as program spectra) to find a statistical relationship between source code locations and observed failures. Although SFL's modeling costs and computational complexity are minimal, its diagnostic accuracy is inherently limited since no reasoning is used. In contrast to SFL, model-based reasoning approaches use prior knowledge of the program, such as component interconnection and statement semantics, to build a model of the correct behavior of the system. On the one hand, model-based reasoning approaches deliver higher diagnostic accuracy, but on the other hand, they suffer from high computation complexity.

Autorentext

Rui Abreu graduated in Systems and Computer Eng. from Univ. of Minho, and followed courses of the Software Tech Masters at Univ. of Utrecht. He was an intern at Philips Research Labs Oct''04-Jun''05. Received his Ph.D. from Delft Univ. of Tech, in Nov''09, and he is now an assistant professor at Univ. of Porto. He was a Visiting Faculty at CMU.