Arjan J. C. van Gemund

Learn More
Spectrum-based fault localization shortens the testdiagnose-repair cycle by reducing the debugging effort. As a light-weight 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)
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)
0164-1212/$ see front matter 2009 Elsevier Inc. A doi:10.1016/j.jss.2009.06.035 q This work has been carried out as part of the responsibility of the Embedded Systems Institute. This by the Netherlands Ministry of Economic Affairs und * Corresponding author. E-mail addresses: r.f.abreu@tudelft.nl (R. Ab (P. Zoeteweij), rob.golsteijn@nxp.com (R. Golsteijn)(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)
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)
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)
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 parallelism 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)
In this paper we present a new methodology for the performance prediction of parallel programs on parallel platforms ranging from shared-memory to distributed-memory (vector) machines. The methodology comprises a procedural program and machine specification paradigm based on PAMELA (PerformAnce ModEling LAnguage), along with a performance calculus, called(More)