Model-Based Diagnosability Analysis for Web Services
Stefano Bocconi, Claudia Picardi, Xavier Pucel, Daniele Theseider Dupré and Louise Travé-Massuyès
The 10th Congress of the Italian Association for Artificial Intelligence (AIIA 2007)
Roma, Italy, September 10-13, 2007
Abstract
In this paper we deal with the problem of model-based diagnosability analysis for Web Services. The goal of diagnosability analysis is to determine whether the information one can observe during service execution is sufficient to precisely locate (by means of diagnostic reasoning) the source of the problem. The major difficulty in the context of Web Services is that models are distributed and no single entity has a global view of the complete model. In the paper we propose an approach that computes diagnosability for the decentralized diagnostic framework, based on a Supervisor coordinating several Local Diagnosers, described in [Console et al, IJCAI 07]. We also show that diagnosability analysis can be performed without requiring the Local Diagnosers different operations than those needed for diagnosis. The proposed approach is incremental: each fault is first analyzed independently of the occurrence of other faults, then the results are used to analyze combinations of behavioral modes, avoiding in most cases an exhaustive check of all combinations.