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Documents authored by Miné, Antoine

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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