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Data Flow Analysis of Asynchronous Systems using Infinite Abstract Domains

Athaiya, S and Komondoor, R and Kumar, KN (2021) Data Flow Analysis of Asynchronous Systems using Infinite Abstract Domains. In: 30th European Symposium on Programming, ESOP 2021, 27 March - 1 April 2021, Luxembourg, pp. 30-58.

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Official URL: https://doi.org/10.1007/978-3-030-72019-3_2


Asynchronous message-passing systems are employed frequently to implement distributed mechanisms, protocols, and processes. This paper addresses the problem of precise data flow analysis for such systems. To obtain good precision, data flow analysis needs to somehow skip execution paths that read more messages than the number of messages sent so far in the path, as such paths are infeasible at run time. Existing data flow analysis techniques do elide a subset of such infeasible paths, but have the restriction that they admit only finite abstract analysis domains. In this paper we propose a generalization of these approaches to admit infinite abstract analysis domains, as such domains are commonly used in practice to obtain high precision. We have implemented our approach, and have analyzed its performance on a set of 14 benchmarks. On these benchmarks our tool obtains significantly higher precision compared to a baseline approach that does not elide any infeasible paths and to another baseline that elides infeasible paths but admits only finite abstract domains. © 2021, The Author(s).

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Benchmarking; Data transfer; Message passing, Abstract domains; Asynchronous system; Execution paths; High-precision; Infeasible paths; Message passing systems; Runtimes, Data flow analysis
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 02 Jun 2023 10:06
Last Modified: 02 Jun 2023 10:06
URI: https://eprints.iisc.ac.in/id/eprint/81746

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