Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Conflict-directed A* and its role in model-based embedded systems
Discrete Applied Mathematics
On the Accuracy of Spectrum-based Fault Localization
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
An observation-based model for fault localization
WODA '08 Proceedings of the 2008 international workshop on dynamic analysis: held in conjunction with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2008)
Computing minimal diagnoses by greedy stochastic search
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Pervasive diagnosis: the integration of diagnostic goals into production plans
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Evaluating Models for Model-Based Debugging
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Zoltar: a spectrum-based fault localization tool
Proceedings of the 2009 ESEC/FSE workshop on Software integration and evolution @ runtime
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
Compiling AI engineering models for probabilistic inference
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Diagnosing architectural run-time failures
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
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Logic reasoning approaches to fault diagnosis account for the fact that a component cj may fail intermittently by introducing a parameter gj that expresses the probability the component exhibits correct behavior. This component parameter gj, in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on gj is not known a priori. While proper estimation of gj can have a great impact on the diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, BARINEL, that computes exact estimations of gj as integral part of the posterior candidate probability computation. BARINEL's diagnostic performance is evaluated for both synthetic and real software systems. Our results show that our approach is superior to approaches based on classical persistent fault models as well as previously proposed intermittent fault models.