Arjan J. C. van Gemund

Learn 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)
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 pure task paral-lelism or pure data parallelism. In this paper we present a new compile-time(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)
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)
The Distributed ASCI Supercomputer (DAS) is a homogeneous wide-area distributed system consisting of four cluster computers at different locations. DAS has been used for research on communication software, parallel languages and programming systems, schedulers, parallel applications, and distributed applications. The paper gives a preview of the most(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)
For many large systems the computational complexity of complete model-based diagnosis is prohibitive. In this paper we investigate the speedup of the diagnosis process by exploiting the hierarchy/locality as is typically present in well-engineered systems. The approach comprises a compile-time and a run-time step. In the first step, a hierarchical CNF(More)
Recently a new parallel programming model has been presented that imposes synchronization restrictions in order to allow for fully automatic, retargetable program optimization. The motivation for the model is the conjecture that in practice the loss of parallel-ism due to the inherent synchronization restrictions is less than a factor of 2. In this paper we(More)