A Distributed Approach to Diagnosis Candidate Generation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

Nuno Cardoso (Autor) Outra; Rui Abreu (Autor) FEUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2013

Páginas: 175-186

16th Portuguese Conference on Artificial Intelligence (EPIA)

Angra do Heroismo, PORTUGAL, SEP 09-12, 2013

Indexação

ISI Proceedings

ISI Web of Knowledge - 2 Citações

ISI Web of Science

Scopus - 4 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-008-EFR

DOI: 10.1007/978-3-642-40669-0_16

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12