Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
A Circumscribed Diagnosis Engine
Proceedings of the International Workshop on Expert Systems in Engineering, Principles and Applications
Diagnosis of Intermittent Faults in Combinational Networks
IEEE Transactions on Computers
Exploiting count spectra for Bayesian fault localization
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Diagnosing multiple intermittent failures using maximum likelihood estimation
Artificial Intelligence
An empirical study on the usage of testability information to fault localization in software
Proceedings of the 2011 ACM Symposium on Applied Computing
A diagnostic reasoning approach to defect prediction
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Architecture-based run-time fault diagnosis
ECSA'11 Proceedings of the 5th European conference on Software architecture
Annals of Mathematics and Artificial Intelligence
Prioritizing tests for fault localization through ambiguity group reduction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Diagnosing architectural run-time failures
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
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Almost all approaches to model-based diagnosis presume that the system being diagnosed behaves non-intermittently and analyze behavior over a small number (often only one) of time instants. In this paper we show how existing approaches to model-based diagnosis can be extended to diagnose intermittent failures as they manifest themselves over time. In addition, we show where to insert probe points to best distinguish among the intermittent faults those that best explain the symptoms and isolate the fault in minimum expected cost.