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Current approaches to automatic software fault localization can be classified as either (1) statisticsbased approaches, or (2) reasoning approaches. This distinction is based on the required amount of knowledge about the program's internal component structure and behavior. Statisticsbased fault localization techniques such as Spectrumbased 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, modelbased 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, modelbased reasoning approaches deliver higher diagnostic accuracy, but on the other hand, they suffer from high computation complexity.
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