Complexity results for structure-based causality
Artificial Intelligence
Automatic Synthesis of Dynamic Fault Trees from UML System Models
ISSRE '02 Proceedings of the 13th International Symposium on Software Reliability Engineering
Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
A formal approach to fault tree synthesis for the analysis of distributed fault tolerant systems
Proceedings of the 5th ACM international conference on Embedded software
Debugging of Dependability Models Using Interactive Visualization of Counterexamples
QEST '08 Proceedings of the 2008 Fifth International Conference on Quantitative Evaluation of Systems
Model Based Importance Analysis for Minimal Cut Sets
ATVA '08 Proceedings of the 6th International Symposium on Automated Technology for Verification and Analysis
Counterexample Generation in Probabilistic Model Checking
IEEE Transactions on Software Engineering
Explaining Counterexamples Using Causality
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Safety Analysis of an Airbag System Using Probabilistic FMEA and Probabilistic Counterexamples
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Why Programs Fail, Second Edition: A Guide to Systematic Debugging
Why Programs Fail, Second Edition: A Guide to Systematic Debugging
IEEE Transactions on Software Engineering
Symbolic fault tree analysis for reactive systems
ATVA'07 Proceedings of the 5th international conference on Automated technology for verification and analysis
Automatic fault tree derivation from Little-JIL process definitions
SPW/ProSim'06 Proceedings of the 2006 international conference on Software Process Simulation and Modeling
PRISM: a tool for automatic verification of probabilistic systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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In recent years, several approaches to generate probabilistic counterexamples have been proposed. The interpretation of stochastic counterexamples, however, continues to be problematic since they have to be represented as sets of paths, and the number of paths in this set may be very large. Fault trees (FTs) are a well-established industrial technique to represent causalities for possible system hazards resulting from system or system component failures. In this paper we suggest a method to automatically derive FTs from counterexamples, including a mapping of the probability information onto the FT. We extend the structural equation approach by Pearl and Halpern, which is based on Lewis counterfactuals, so that it serves as a justification for the causality that our proposed FT derivation rules imply. We demonstrate the usefulness of our approach by applying it to an industrial case study.