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A Multilanguage Static Analysis of Python Programs with Native C Extensions

Authors: Monat, Raphaël; Ouadjaout, Abdelraouf; Miné, Antoine;

A Multilanguage Static Analysis of Python Programs with Native C Extensions

Abstract

Modern programs are increasingly multilanguage, to benefit from each programming language’s advantages and to reuse libraries. For example, developers may want to combine high-level Python code with low-level, performance-oriented C code. In fact, one in five of the 200 most downloaded Python libraries available on GitHub contains C code. Static analyzers tend to focus on a single language and may use stubs to model the behavior of foreign function calls. However, stubs are costly to implement and undermine the soundness of analyzers. In this work, we design a static analyzer by abstract interpretation that can handle Python programs calling C extensions. It analyses directly and fully automatically both the Python and the C source codes. It reports runtime errors that may happen in Python, in C, and at the interface. We implemented our analysis in a modular fashion: it reuses off-the-shelf C and Python analyses written in the same analyzer. This approach allows sharing between abstract domains of different languages. Our analyzer can tackle tests of real-world libraries a few thousand lines of C and Python long in a few minutes.

Country
France
Keywords

Multilanguage Analysis, Abstract Interpretation, Static Analysis, Dynamic Programming Language, Formal Methods, [INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
29
Top 10%
Top 10%
Top 10%
Green