Complementarity of Error Detection Techniques
Electronic Notes in Theoretical Computer Science (ENTCS)
Guided model checking for programs with polymorphism
Proceedings of the 2009 ACM SIGPLAN workshop on Partial evaluation and program manipulation
A Meta Heuristic for Effectively Detecting Concurrency Errors
HVC '08 Proceedings of the 4th International Haifa Verification Conference on Hardware and Software: Verification and Testing
Efficient Testing of Concurrent Programs with Abstraction-Guided Symbolic Execution
Proceedings of the 16th International SPIN Workshop on Model Checking Software
Clash of the Titans: tools and techniques for hunting bugs in concurrent programs
Proceedings of the 7th Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
Vector-clock based partial order reduction for JPF
ACM SIGSOFT Software Engineering Notes
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Directed model checking algorithms focus computation resources in the error-prone areas of concurrent systems. The algorithms depend on some empirical analysis to report their performance gains. Recent work characterizes the hardness of models used in the analysis as an estimated number of paths in the model that contain an error. This hardness metric is computed using a stateless random walk. We show that this is not a good hardness metric because models labeled hard with a stateless random walk metric have easily discoverable errors with a stateful randomized search. We present an analysis which shows that a hardness metric based on a stateful randomized search is a tighter bound for hardness in models used to benchmark explicit state directed model checking techniques. Furthermore, we convert easy models into hard models as measured by our new metric by pushing the errors deeper in the system and manipulating the number of threads that actually manifest an error.