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.