All-du-path coverage for parallel programs
Proceedings of the 1998 ACM SIGSOFT international symposium on Software testing and analysis
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IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Heuristics for Finding Concurrent Bugs
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
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Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Delivering Mobile Enterprise Services on Morfeo's MC Open Source Platform
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Forcing small models of conditions on program interleaving for detection of concurrent bugs
Proceedings of the 7th Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
Multithreaded java program test generation
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Electronic Notes in Theoretical Computer Science (ENTCS)
Explaining intermittent concurrent bugs by minimizing scheduling noise
HVC'06 Proceedings of the 2nd international Haifa verification conference on Hardware and software, verification and testing
A platform for search-based testing of concurrent software
Proceedings of the 8th Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
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SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
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Testing of concurrent software is difficult due to the non-determinism present in scheduling of concurrent threads. Existing testing approaches tackle this problem either using a modified scheduler which allows to systematically explore possible scheduling alternatives or using random or heuristic noise injection which allows to observe different scheduling scenarios. In this paper, we experimentally compare several existing noise injection heuristics both from the point of view of coverage of possible behaviours as well as from the point of view of error discovery probability. Moreover, we also propose a new noise injection heuristics which uses concurrency coverage information to decide where to put noise and show that it can outperform the existing approaches in certain cases.