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ACM Transactions on Programming Languages and Systems (TOPLAS)
IEEE Transactions on Software Engineering
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STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
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PPOPP '91 Proceedings of the third ACM SIGPLAN symposium on Principles and practice of parallel programming
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ACM Transactions on Computer Systems (TOCS)
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Efficient Detection of Global Properties in Distributed Systems Using Partial-Order Methods
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Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
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jPredictor: a predictive runtime analysis tool for java
Proceedings of the 30th international conference on Software engineering
Dynamic testing of flow graph based parallel applications
PADTAD '08 Proceedings of the 6th workshop on Parallel and distributed systems: testing, analysis, and debugging
Dynamic Model Checking with Property Driven Pruning to Detect Race Conditions
ATVA '08 Proceedings of the 6th International Symposium on Automated Technology for Verification and Analysis
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Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
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FM '09 Proceedings of the 2nd World Congress on Formal Methods
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RV'10 Proceedings of the First international conference on Runtime verification
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RV'10 Proceedings of the First international conference on Runtime verification
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SAS'11 Proceedings of the 18th international conference on Static analysis
Sound predictive race detection in polynomial time
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Universal causality graphs: a precise happens-before model for detecting bugs in concurrent programs
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
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Happen-before causal partial orders have been widely used in concurrent program verification and testing. This paper presents a parametric approach to happen-before causal partial orders. Existing variants of happen-before relations can be obtained as instances of the parametric framework. A novel causal partial order, called sliced causality, is then defined also as an instance of the parametric framework, which loosens the obvious but strict happen-before relation by considering static and dynamic dependence information about the program. Sliced causality has been implemented in a runtime predictive analysis tool for JAVA, named jPREDICTOR, and the evaluation results show that sliced causality can significantly improve the capability of concurrent verification and testing.