Static analysis for dummies: experiencing LiSA

Ferrara, Pietro; Negrini, Luca; Arceri, Vincenzo; Cortesi, Agostino

doi:10.1145/3460946.3464316

Cited by 17 publications

(3 citation statements)

References 8 publications

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“…Experiments presented in this paper have been performed using GoLiSA (https://github.com/lisa-analyzer/go-lisa), a static analyzer for Go based on LiSA, 13,14 whose high level infrastructure is visible in Figure 8.…”

Section: Lisa and Golisamentioning

confidence: 99%

“…TARSIS has been implemented in GoLiSA, 12 a static analyzer for Go based on Library for Static Analysis (LiSA). 13,14 By comparing TARSIS with other cutting-edge domains for string analysis, results show that (i) when applied to simple code that causes a precision loss in simpler domains, TARSIS correctly approximates string values within a comparable execution time; (ii) on code that makes the standard automata domain unusable due to the complexity of the analysis, TARSIS is in position to perform in a limited amount of time, making it a viable domain for complex and real codebases; and (iii) TARSIS is able to precisely abstract complex string operations that have not been addressed by state-of-the-art domains.…”

mentioning

confidence: 99%

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Tarsis: An effective automata‐based abstract domain for string analysis

Negrini,

Arceri,

Cortesi

et al. 2024

J Software Evolu Process

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In this paper, we introduce Tarsis, a new abstract domain based on the abstract interpretation theory that approximates string values through finite state automata. The main novelty of Tarsis is that it works over an alphabet of strings instead of single characters. On the one hand, such an approach requires a more complex and refined definition of the lattice operators and of the abstract semantics of string operators. On the other hand, it is in position to obtain strictly more precise results than state‐of‐the‐art approaches. We compare Tarsis both with simpler domains and with the standard automata model, targeting case studies containing standard yet challenging string manipulations. The performance gain w.r.t. the standard automata model is also assessed, measuring the speed‐up gained by Tarsis. Experiments confirm that Tarsis can obtain precise results without incurring in excessive computational costs.

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Section: Lisa and Golisamentioning

confidence: 99%

mentioning

confidence: 99%

Tarsis: An effective automata‐based abstract domain for string analysis

Negrini,

Arceri,

Cortesi

et al. 2024

J Software Evolu Process

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“…LiSA [3,6] (Library for Static Analysis) is a modular framework for developing static analyzers based on the abstract interpretation theory. LiSA analyzes CFGs whose statements do not have predefined semantics: instead, users of the framework define custom statement instances implementing language-specific semantic functions, enabling the analysis of a wide range of programming languages and the development of multilanguage analyses.…”

Section: Lisa and Pylisamentioning

confidence: 99%

Static Analysis of Data Transformations in Jupyter Notebooks

Negrini,

Shabadi,

Urban

2023

Proceedings of the 12th ACM SIGPLAN International Workshop on the State of the Art in Program Analysis

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Jupyter notebooks used to pre-process and polish raw data for data science and machine learning processes are challenging to analyze. Their data-centric code manipulates dataframes through call to library functions with complex semantics, and the properties to track over it vary widely depending on the verification task. This paper presents a novel abstract domain that simplifies writing analyses for such programs, by extracting a unique CFG from the notebook that contains all transformations applied to the data. Several properties can then be determined by analyzing such CFG, that is simpler than the original Python code. We present a first use case that exploits our analysis to infer the required shape of the dataframes manipulated by the notebook.

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