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Documents authored by Miné, Antoine

Document
DOI: 10.4230/LIPIcs.ECOOP.2025.28
Reusing Caches and Invariants for Efficient and Sound Incremental Static Analysis

Authors: Mamy Razafintsialonina, David Bühler, Antoine Miné, Valentin Perrelle, and Julien Signoles

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Static analysis by means of abstract interpretation is a tool of choice for proving absence of some classes of errors, typically undefined behaviors in C code, in a sound way. However, static analysis tools are hardly integrated in CI/CD processes. One of the main reasons is that they are still time- and memory-expensive to apply after every single patch when developing a program. For solving this issue, incremental static analysis helps developers quickly obtain analysis results after making changes to a program. However, existing approaches are often not guaranteed to be sound, limited to specific analyses, or tied to specific tools. This limits their generalizability and applicability in practice, especially for large and critical software. In this paper, we propose a generic, sound approach to incremental static analysis that is applicable to any abstract interpreter. Our approach leverages the similarity between two versions of a program to soundly reuse previously computed analysis results. We introduce novel methods for summarizing functions and reusing loop invariants. They significantly reduce the cost of reanalysis, while maintaining soundness and a high level of precision. We have formalized our approach, proved it sound, implemented it in Eva, the abstract interpreter of Frama-C, and evaluated it on a set of real-world commits of open-source programs.
Cite as

Mamy Razafintsialonina, David Bühler, Antoine Miné, Valentin Perrelle, and Julien Signoles. Reusing Caches and Invariants for Efficient and Sound Incremental Static Analysis. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 28:1-28:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{razafintsialonina_et_al:LIPIcs.ECOOP.2025.28,
  author =	{Razafintsialonina, Mamy and B\"{u}hler, David and Min\'{e}, Antoine and Perrelle, Valentin and Signoles, Julien},
  title =	{{Reusing Caches and Invariants for Efficient and Sound Incremental Static Analysis}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{28:1--28:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.28},
  URN =		{urn:nbn:de:0030-drops-233207},
  doi =		{10.4230/LIPIcs.ECOOP.2025.28},
  annote =	{Keywords: Abstract Interpretation, Static Analysis, Incremental Analysis}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.32
Compositional Static Value Analysis for Higher-Order Numerical Programs

Authors: Milla Valnet, Raphaël Monat, and Antoine Miné

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Static analyzers have been successfully developed to detect runtime errors in many languages. However, the automatic analysis of functional languages remains a challenge due to their recursive functions, recursive algebraic data types, and higher-order functions. Classic type systems provide compositional methods that are in general not precise enough to prove the absence of runtime errors such as assertion failures. At the other end of the spectrum, deductive methods are more expressive but may require user guidance to prove invariants. Our work describes a static value analysis by abstract interpretation for a higher-order pure functional language. This analysis provides a sound and automatic approach to discover invariants and prevent assertion and match failures. We have designed a compositional analysis: functions are analyzed only once, at their definition site, generating a summary of their behavior. The summaries can be viewed as input-output relations expressed with relational abstract domains. We present two new abstract domains. A first abstract domain summarizes recursive algebraic data types. A second abstract domain lifts existing disjunctive relational summaries to higher-order by formalizing them as domains able to abstract higher-order functions. Both abstractions are parameterized by the abstractions of basic types (strings, integers, ...). Thanks to this parametric nature, both domains can be combined, allowing the analysis of higher-order functions manipulating algebraic data types and, conversely, algebraic data types using functions as first-class values. We have implemented this analysis in the open-source MOPSA platform. Preliminary evaluation confirms the precision of our approach on a set of 40 handwritten toy programs as well as 20 programs from the state-of-the-art Salto analyzer benchmark.
Cite as

Milla Valnet, Raphaël Monat, and Antoine Miné. Compositional Static Value Analysis for Higher-Order Numerical Programs. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 32:1-32:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{valnet_et_al:LIPIcs.ECOOP.2025.32,
  author =	{Valnet, Milla and Monat, Rapha\"{e}l and Min\'{e}, Antoine},
  title =	{{Compositional Static Value Analysis for Higher-Order Numerical Programs}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{32:1--32:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.32},
  URN =		{urn:nbn:de:0030-drops-233249},
  doi =		{10.4230/LIPIcs.ECOOP.2025.32},
  annote =	{Keywords: Static Value Analysis, Functional Programming, Abstract Interpretation}
}
Document
Artifact
DOI: 10.4230/DARTS.11.2.5
Compositional Static Value Analysis for Higher-Order Numerical Programs (Artifact)

Authors: Milla Valnet, Raphaël Monat, and Antoine Miné

Published in: DARTS, Volume 11, Issue 2, Special Issue of the 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
This artifact accompanies the submission entitled "Compositional Static Value Analysis for Higher-Order Numerical Programs". It enables to reproduce experimental claims made in the paper. The artifact requires a computer with Docker installed.
Cite as

Milla Valnet, Raphaël Monat, and Antoine Miné. Compositional Static Value Analysis for Higher-Order Numerical Programs (Artifact). In Special Issue of the 39th European Conference on Object-Oriented Programming (ECOOP 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 2, pp. 5:1-5:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{valnet_et_al:DARTS.11.2.5,
  author =	{Valnet, Milla and Monat, Rapha\"{e}l and Min\'{e}, Antoine},
  title =	{{Compositional Static Value Analysis for Higher-Order Numerical Programs (Artifact)}},
  pages =	{5:1--5:5},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2025},
  volume =	{11},
  number =	{2},
  editor =	{Valnet, Milla and Monat, Rapha\"{e}l and Min\'{e}, Antoine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.2.5},
  URN =		{urn:nbn:de:0030-drops-233481},
  doi =		{10.4230/DARTS.11.2.5},
  annote =	{Keywords: Static Value Analysis, Functional Programming, Abstract Interpretation}
}
Document
DOI: 10.4230/DagRep.13.7.66
Theoretical Advances and Emerging Applications in Abstract Interpretation (Dagstuhl Seminar 23281)

Authors: Arie Gurfinkel, Isabella Mastroeni, Antoine Miné, Peter Müller, and Anna Becchi

Published in: Dagstuhl Reports, Volume 13, Issue 7 (2024)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23281 "Theoretical Advances and Emerging Applications in Abstract Interpretation." Abstract Interpretation (AI) is a theory of the approximation of program semantics. Since its introduction in the 70s, it lead to insights into theoretical research in semantics, a rich and robust mathematical framework to discuss about semantic approximation and program analysis, and the design of effective program analysis tools that are now routinely used in this industry. The seminar brought together academic and industrial partners to assess the state of the art in AI as well as discuss its future. It considered its foundational aspects, connections with other formal methods, emergent applications, user needs in program verification, tool design and evaluation, as well as educational aspects and community management. Its goal was to collect new ideas and new perspectives on all these aspects of AI in order to pave the way for new applications.
Cite as

Arie Gurfinkel, Isabella Mastroeni, Antoine Miné, Peter Müller, and Anna Becchi. Theoretical Advances and Emerging Applications in Abstract Interpretation (Dagstuhl Seminar 23281). In Dagstuhl Reports, Volume 13, Issue 7, pp. 66-95, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{gurfinkel_et_al:DagRep.13.7.66,
  author =	{Gurfinkel, Arie and Mastroeni, Isabella and Min\'{e}, Antoine and M\"{u}ller, Peter and Becchi, Anna},
  title =	{{Theoretical Advances and Emerging Applications in Abstract Interpretation (Dagstuhl Seminar 23281)}},
  pages =	{66--95},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{7},
  editor =	{Gurfinkel, Arie and Mastroeni, Isabella and Min\'{e}, Antoine and M\"{u}ller, Peter and Becchi, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.7.66},
  URN =		{urn:nbn:de:0030-drops-197759},
  doi =		{10.4230/DagRep.13.7.66},
  annote =	{Keywords: abstract domains, abstract interpretation, program semantics, program verification, static program analysis}
}
Document
Artifact
DOI: 10.4230/DARTS.6.2.11
Static Type Analysis by Abstract Interpretation of Python Programs (Artifact)

Authors: Raphaël Monat, Abdelraouf Ouadjaout, and Antoine Miné

Published in: DARTS, Volume 6, Issue 2, Special Issue of the 34th European Conference on Object-Oriented Programming (ECOOP 2020)

Abstract
Python is an increasingly popular dynamic programming language, particularly used in the scientific community and well-known for its powerful and permissive high-level syntax. Our work aims at detecting statically and automatically type errors. As these type errors are exceptions that can be caught later on, we precisely track all exceptions (raised or caught). We designed a static analysis by abstract interpretation able to infer the possible types of variables, taking into account the full control-flow. It handles both typing paradigms used in Python, nominal and structural, supports Python’s object model, introspection operators allowing dynamic type testing, dynamic attribute addition, as well as exception handling. We present a flow- and context-sensitive analysis with special domains to support containers (such as lists) and infer type equalities (allowing it to express parametric polymorphism). The analysis is soundly derived by abstract interpretation from a concrete semantics of Python developed by Fromherz et al. Our analysis is designed in a modular way as a set of domains abstracting a concrete collecting semantics. It has been implemented into the MOPSA analysis framework, and leverages external type annotations from the Typeshed project to support the vast standard library. We show that it scales to benchmarks a few thousand lines long, and preliminary results show it is able to analyze a small real-life command-line utility called PathPicker. Compared to previous work, it is sound, while it keeps similar efficiency and precision.
Cite as

Raphaël Monat, Abdelraouf Ouadjaout, and Antoine Miné. Static Type Analysis by Abstract Interpretation of Python Programs (Artifact). In Special Issue of the 34th European Conference on Object-Oriented Programming (ECOOP 2020). Dagstuhl Artifacts Series (DARTS), Volume 6, Issue 2, pp. 11:1-11:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{monat_et_al:DARTS.6.2.11,
  author =	{Monat, Rapha\"{e}l and Ouadjaout, Abdelraouf and Min\'{e}, Antoine},
  title =	{{Static Type Analysis by Abstract Interpretation of Python Programs (Artifact)}},
  pages =	{11:1--11:6},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2020},
  volume =	{6},
  number =	{2},
  editor =	{Monat, Rapha\"{e}l and Ouadjaout, Abdelraouf and Min\'{e}, Antoine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.6.2.11},
  URN =		{urn:nbn:de:0030-drops-132082},
  doi =		{10.4230/DARTS.6.2.11},
  annote =	{Keywords: Formal Methods, Static Analysis, Abstract Interpretation, Type Analysis, Dynamic Programming Language, Python Semantics}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2020.17
Static Type Analysis by Abstract Interpretation of Python Programs

Authors: Raphaël Monat, Abdelraouf Ouadjaout, and Antoine Miné

Published in: LIPIcs, Volume 166, 34th European Conference on Object-Oriented Programming (ECOOP 2020)

Abstract
Python is an increasingly popular dynamic programming language, particularly used in the scientific community and well-known for its powerful and permissive high-level syntax. Our work aims at detecting statically and automatically type errors. As these type errors are exceptions that can be caught later on, we precisely track all exceptions (raised or caught). We designed a static analysis by abstract interpretation able to infer the possible types of variables, taking into account the full control-flow. It handles both typing paradigms used in Python, nominal and structural, supports Python’s object model, introspection operators allowing dynamic type testing, dynamic attribute addition, as well as exception handling. We present a flow- and context-sensitive analysis with special domains to support containers (such as lists) and infer type equalities (allowing it to express parametric polymorphism). The analysis is soundly derived by abstract interpretation from a concrete semantics of Python developed by Fromherz et al. Our analysis is designed in a modular way as a set of domains abstracting a concrete collecting semantics. It has been implemented into the MOPSA analysis framework, and leverages external type annotations from the Typeshed project to support the vast standard library. We show that it scales to benchmarks a few thousand lines long, and preliminary results show it is able to analyze a small real-life command-line utility called PathPicker. Compared to previous work, it is sound, while it keeps similar efficiency and precision.
Cite as

Raphaël Monat, Abdelraouf Ouadjaout, and Antoine Miné. Static Type Analysis by Abstract Interpretation of Python Programs. In 34th European Conference on Object-Oriented Programming (ECOOP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 166, pp. 17:1-17:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{monat_et_al:LIPIcs.ECOOP.2020.17,
  author =	{Monat, Rapha\"{e}l and Ouadjaout, Abdelraouf and Min\'{e}, Antoine},
  title =	{{Static Type Analysis by Abstract Interpretation of Python Programs}},
  booktitle =	{34th European Conference on Object-Oriented Programming (ECOOP 2020)},
  pages =	{17:1--17:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-154-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{166},
  editor =	{Hirschfeld, Robert and Pape, Tobias},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2020.17},
  URN =		{urn:nbn:de:0030-drops-131748},
  doi =		{10.4230/LIPIcs.ECOOP.2020.17},
  annote =	{Keywords: Formal Methods, Static Analysis, Abstract Interpretation, Type Analysis, Dynamic Programming Language, Python Semantics}
}
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