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Spectrum-based fault localization shortens the test-diagnose-repair cycle by reducing the debugging effort. As a lightweight automated diagnosis technique it can easily be integrated with existing testing schemes. However , as no model of the system is taken into account, its diagnostic accuracy is inherently limited. Using the Siemens Set benchmark, we(More)
Keywords: Test data analysis Software fault diagnosis Program spectra Real-time and embedded systems Consumer electronics a b s t r a c t Spectrum-based fault localization (SFL) shortens the test–diagnose–repair cycle by reducing the debug-ging effort. As a lightweight automated diagnosis technique it can easily be integrated with existing testing schemes.(More)
Automated diagnosis of software faults can improve the efficiency of the debugging process, and is therefore an important technique for the development of dependable software. In this paper we study different similarity coefficients that are applied in the context of a program spectral approach to software fault localization (single programming mistakes).(More)
Fault diagnosis approaches can generally be categorized into spectrum-based fault localization (SFL, correlating failures with abstractions of program traces), and model-based diagnosis (MBD, logic reasoning over a behavioral model). Although MBD approaches are inherently more accurate than SFL, their high computational complexity prohibits application to(More)
A relatively new trend in parallel programming scheduling is the so-called mixed task and data scheduling. It has been shown that mixing task and data parallelism to solve large computational applications often yields better speedups compared to either applying more task parallelism or pure data parallelism. In this paper we present a new compile-time(More)
Recently, we presented two very low-cost approaches to compile-time list scheduling where the tasks' priorities are computed statically or dynamically, respectively. For homogeneous systems, these two algorithms, called FCP and FLB, have shown to yield a performance equivalent to other much more costly algorithms such as MCP and ETF. In this paper we(More)
It is well-known that mixing task and data parallelism to solve large computational applications often yields better speedups compared to either applying pure task paral-lelism or pure data parallelism. Typically, the applications are modeled in terms of a dependence graph of coarse-grain data-parallel tasks, called a data-parallel task graph. In this paper(More)
—Performance prediction is an important engineering tool that provides valuable feedback on design choices in program synthesis and machine architecture development. We present an analytic performance modeling approach aimed to minimize prediction cost, while providing a prediction accuracy that is sufficient to enable major code and data mapping decisions.(More)
Generating minimal hitting sets of a collection of sets is known to be NP-hard, necessitating heuristic approaches to handle large problems. In this paper a low-cost, approximate minimal hitting set (MHS) algorithm, coined STACCATO, is presented. STACCATO uses a heuristic function, borrowed from a lightweight, statistics-based software fault localiza-tion(More)
We propose a StochAstic Fault diagnosis AlgoRIthm, called Safari, which trades off guarantees of computing minimal diagnoses for computational efficiency. We empirically demonstrate, using the 74XXX and ISCAS85 suites of benchmark combinatorial circuits, that Safari achieves several orders-of-magnitude speedup over two well-known determinis-tic algorithms,(More)