Leveraging Qualitative Reasoning to Improve SFL

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Perez, A (Autor) Outra; Rui Abreu (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden.

Páginas: 1935-1941

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00N-ZTD

DOI: 10.24963/ijcai.2018/267

Abstract (EN): Spectrum-based fault localization (SFL) correlates a system's components with observed failures. By reasoning about coverage, SFL allows for a lightweight way of pinpointing faults. This abstraction comes at the cost of missing certain faults, such as errors of omission, and failing to provide enough contextual information to explain why components are considered suspicious. We propose an approach, named Q-SFL, that leverages qualitative reasoning to augment the information made available to SFL techniques. It qualitatively partitions system components, and treats each qualitative state as a new SFL component to be used when diagnosing. Our empirical evaluation shows that augmenting SFL with qualitative components can improve diagnostic accuracy in 54% of the considered real-world subjects. In Memoriam: Danny Bobrow. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.

Idioma: Inglês

Tipo (Avaliação Docente): Científica