<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cardoso, Nuno</style></author><author><style face="normal" font="default" size="100%">Rui Abreu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Distributed Approach to Diagnosis Candidate Generation</style></title><secondary-title><style face="normal" font="default" size="100%">Progress in Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Angra do Heroísmo</style></pub-location><pages><style face="normal" font="default" size="100%">175–186</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Generating diagnosis candidates for a set of failing transactions is an important challenge in the context of automatic fault localization of both software and hardware systems. Being an NP-Hard problem, exhaustive algorithms are usually prohibitive for real-world, often large, problems. In practice, the usage of heuristic-based approaches trade-off completeness for time efficiency. An example of such heuristic approaches is Staccato, which was proposed in the context of reasoning-based fault localization. In this paper, we propose an efficient distributed algorithm, dubbed MHS2, that renders the sequential search algorithm Staccato suitable to distributed, Map-Reduce environments. The results show that MHS2 scales to larger systems (when compared to Staccato), while entailing either marginal or small runtime overhead.&lt;/p&gt;
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