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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Chen, F., & Roşu, G. (2007). Parametric and sliced causality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4590 LNCS, pp. 240–253). Springer Verlag. https://doi.org/10.1007/978-3-540-73368-3_27
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