Shape analysis by graph decomposition

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Abstract

Programs commonly maintain multiple linked data structures. Correlations between multiple data structures may often be nonexistent or irrelevant to verifying that the program satisfies certain safety properties or invariants. In this paper, we show how this independence between different (singly-linked) data structures can be utilized to perform shape analysis and verification more efficiently. We present a new abstraction based on decomposing graphs into sets of subgraphs, and show that, in practice, this new abstraction leads to very little loss of precision, while yielding substantial improvements to efficiency. © Springer-Verlag Berlin Heidelberg 2007.

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APA

Manevich, R., Berdine, J., Cook, B., Ramalingam, G., & Sagiv, M. (2007). Shape analysis by graph decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4424 LNCS, pp. 3–18). Springer Verlag. https://doi.org/10.1007/978-3-540-71209-1_3

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