@conference {abreu2009statistics,
	title = {Statistics-directed Minimal Hitting Set Algorithm},
	booktitle = {Proceedings of the Twentieth International Workshop on Principles of Diagnosis (DX{\textquoteright}09)},
	year = {2009},
	month = {June},
	pages = {51{\textendash}58},
	address = {Stockholm, Sweden},
	abstract = {<p>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 localization approach, to guide the MHS search. Given the nature of the heuristic function, Staccato is specially tailored to model-based diagnosis problems (where each MHS solution is a diagnosis to the problem), although well-suited for other application domains as well. We apply Staccato in the context of model-based diagnosis and show that even for small problems our approach is orders of magnitude faster than the brute-force approach, while still capturing all important solutions. Furthermore, due to its low cost complexity, we also show that Staccato is amenable to large problems including millions of variables.</p>
},
	attachments = {https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/sara09.pdf},
	author = {Rui Abreu and Van Gemund, Arjan JC}
}