6 Search Results for "Monat, Raphaël"

Issue

DARTS, Volume 10, Issue 2

Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Editors: Karine Even-Mendoza and Raphaël Monat

Document
DOI: 10.4230/LIPIcs.ECOOP.2024.9
A Language-Based Version Control System for Python

Authors: Luís Carvalho and João Costa Seco

Published in: LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
We extend prior work on a language-based approach to versioned software development to support versioned programs with mutable state and evolving method interfaces. Unlike the traditional approach of mainstream version control systems, where a textual diff represents each evolution step, we treat versions as programming elements. Each evolution step, merge operation, and version relationship is represented explicitly in a multifaceted code representation. This provides static guarantees for safe code reuse from previous versions and forward and backwards compatibility between versions, allowing clients to use newly introduced code without needing to refactor their program manually. By lifting versioning to the language level, we pave the way for tools that interact with software repositories to have more insight into a system’s behavior evolution. We instantiate our work in the Python programming language and demonstrate its applicability regarding common evolution and refactoring patterns found in different versions of popular Python packages.
Cite as

Luís Carvalho and João Costa Seco. A Language-Based Version Control System for Python. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 9:1-9:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{carvalho_et_al:LIPIcs.ECOOP.2024.9,
  author =	{Carvalho, Lu{\'\i}s and Costa Seco, Jo\~{a}o},
  title =	{{A Language-Based Version Control System for Python}},
  booktitle =	{38th European Conference on Object-Oriented Programming (ECOOP 2024)},
  pages =	{9:1--9:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-341-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{313},
  editor =	{Aldrich, Jonathan and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.9},
  URN =		{urn:nbn:de:0030-drops-208586},
  doi =		{10.4230/LIPIcs.ECOOP.2024.9},
  annote =	{Keywords: Software evolution, type theory}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2024.23
InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference

Authors: Senxi Li, Tetsuro Yamazaki, and Shigeru Chiba

Published in: LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
Supporting automatic type inference is in demand in modern language development. It is a challenging task but without appropriate supporting toolkits. This paper presents InferType, a Java library that helps implement constraint-based type inference. A compiler writer uses InferType’s classes and methods to describe type constraints and typing rules for type inference. InferType then performs constraint solving by translation to the Z3 SMT solver. InferType is equipped with our developed optimization technique. It reduces the search space for type variables by pre-computing the structures of those type variables for mitigating the performance bottleneck of constraint solving with deeply nested types. We use InferType to implement type inference for a subset of Python, and conduct experiments to evaluate how the developed optimization technique can affect the performance of type inference. Our results show that InferType’s optimization can greatly mitigate the performance bottleneck for programs with deeply nested types, and can potentially improve the performance for large nested types.
Cite as

Senxi Li, Tetsuro Yamazaki, and Shigeru Chiba. InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 23:1-23:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{li_et_al:LIPIcs.ECOOP.2024.23,
  author =	{Li, Senxi and Yamazaki, Tetsuro and Chiba, Shigeru},
  title =	{{InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference}},
  booktitle =	{38th European Conference on Object-Oriented Programming (ECOOP 2024)},
  pages =	{23:1--23:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-341-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{313},
  editor =	{Aldrich, Jonathan and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.23},
  URN =		{urn:nbn:de:0030-drops-208728},
  doi =		{10.4230/LIPIcs.ECOOP.2024.23},
  annote =	{Keywords: Domain Specific Languages, Compilation, Static Analysis, Type Inference, Constraint Solving, SMT Solver}
}
Document
Front Matter
DOI: 10.4230/DARTS.10.2.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Karine Even-Mendoza and Raphaël Monat

Published in: DARTS, Volume 10, Issue 2, Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024). Dagstuhl Artifacts Series (DARTS), Volume 10, Issue 2, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{evenmendoza_et_al:DARTS.10.2.0,
  author =	{Even-Mendoza, Karine and Monat, Rapha\"{e}l},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  pages =	{0:i--0:xii},
  journal =	{Dagstuhl Artifacts Series},
  ISBN =	{978-3-95977-342-3},
  ISSN =	{2509-8195},
  year =	{2024},
  volume =	{10},
  number =	{2},
  editor =	{Even-Mendoza, Karine and Monat, Rapha\"{e}l},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.10.2.0},
  URN =		{urn:nbn:de:0030-drops-208982},
  doi =		{10.4230/DARTS.10.2.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
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}
}
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}
}
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