Improving Dynamic Code Analysis by Code Abstraction
Isabella Mastroeni (Department of Computer Science, University of, Verona (Italy)), Vincenzo Arceri (Department of Environmental Sciences,, Informatics, Statistics, Ca' Foscari University of Venice (Italy))
TL;DR
This paper introduces a code abstraction model based on abstract interpretation to enhance the precision of static analysis for dynamic languages, addressing spurious code issues.
Contribution
It presents a novel code abstraction approach driven by the analysis process, improving static analysis accuracy for dynamic languages.
Findings
Enhanced analysis precision for dynamic languages.
Reduction of spurious code in static analysis.
Improved abstract interpretation techniques.
Abstract
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.
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