A Framework for Automatic Debugging of Functional and Degradation Failures

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Cardoso, N (Autor) Outra; Rui Abreu (Autor) FEUP; Feldman, A (Autor) Outra; de Kleer, J (Autor) Outra

Ata de Conferência Internacional

Título: ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE

Páginas: 569-576

22nd European Conference on Artificial Intelligence (ECAI)

Hague, NETHERLANDS, AUG 29-SEP 02, 2016

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00K-SS1

DOI: 10.3233/978-1-61499-672-9-569

Abstract (EN): Software diagnosis is a particularly challenging problem for modern systems, which may consist of dozens, if not hundreds, of components computing on concurrent and potentially distributed platforms, and using infrastructure and services built by many organizations. We propose a framework that generalizes state-of-the-art classical reasoning-based fault diagnosis which tolerates observation uncertainty and addresses degradation of quality of service. Empirical evaluation involving 27 000 highly realistic synthetic scenarios demonstrates an average accuracy improvement of 20% (with 99% statistical significance) which is considerable in the domain of Software Fault Localization (SFL). We measure the improvement in accuracy on well-established SFL performance metrics.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8