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.
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