@inbook {cardoso2013distributed,
	title = {A Distributed Approach to Diagnosis Candidate Generation},
	booktitle = {Progress in Artificial Intelligence},
	year = {2013},
	pages = {175{\textendash}186},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Angra do Hero{\'\i}smo},
	abstract = {<p>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.</p>
},
	author = {Cardoso, Nuno and Rui Abreu}
}