A Distributed Approach to Diagnosis Candidate Generation

Cardoso N, Abreu R.  2013.  A Distributed Approach to Diagnosis Candidate Generation. Progress in Artificial Intelligence. :175–186.


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.

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