A theory of diagnosis from first principles
Artificial Intelligence
Efficiently computing static single assignment form and the control dependence graph
ACM Transactions on Programming Languages and Systems (TOPLAS)
Symbolic execution and program testing
Communications of the ACM
On the relationship between model-based debugging and program slicing
Artificial Intelligence
Efficient Computation of Recurrence Diameters
VMCAI 2003 Proceedings of the 4th International Conference on Verification, Model Checking, and Abstract Interpretation
Enhancing Web Services with Diagnostic Capabilities
ECOWS '05 Proceedings of the Third European Conference on Web Services
Evaluating Models for Model-Based Debugging
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Exception Handling for Repair in Service-Based Processes
IEEE Transactions on Software Engineering
Compensation in the world of web services composition
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
Exception handling in web service processes
The evolution of conceptual modeling
Reasoning on partially-ordered observations in online diagnosis of DESs
AI Communications
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Diagnosis of process executions is an important task in many application domains, especially in the area of workflow management systems and orchestrated Web Services. If executions fail because activities of the process do not behave as intended, recovery procedures re-execute some activities to recover from the failure. We present a diagnosis method for identifying incorrect activities in process executions. Our method is novel both in that it does not require exact behavioral models for the activities and that its accuracy improves upon dependency-based methods. Observations obtained from partial executions and re-executions of a process are exploited. We formally characterize the diagnosis problem and develop a symbolic encoding that can be solved using CLP(FD) solvers. Our evaluation demonstrates that the framework yields superior accuracy to dependency-based methods on realistically-sized examples.