Improving Dynamic Code Analysis by Code Abstraction

Isabella Mastroeni
(Department of Computer Science, University of Verona (Italy))
Vincenzo Arceri
(Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice (Italy))

In this paper, our aim is to propose a model for code abstraction, based on abstract interpretation, allowing us to improve the precision of a recently proposed static analysis by abstract interpretation of dynamic languages. The problem we tackle here is that the analysis may add some spurious code to the string-to-execute abstract value and this code may need some abstract representations in order to make it analyzable. This is precisely what we propose here, where we drive the code abstraction by the analysis we have to perform.

In Alexei Lisitsa and Andrei P. Nemytykh: Proceedings of the 9th International Workshop on Verification and Program Transformation (VPT 2021), Luxembourg, Luxembourg, 27th and 28th of March 2021, Electronic Proceedings in Theoretical Computer Science 341, pp. 17–32.
Published: 6th September 2021.

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