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Documents authored by Liénard, Julien

Artifact
Software
DOI: 10.4230/artifacts.24594
Pyttern

Authors: Julien Liénard

Abstract
Cite as

Julien Liénard. Pyttern (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24594,
   title = {{Pyttern}}, 
   author = {Li\'{e}nard, Julien},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:1e349725fe7d18d7785112e0599682d5e7508ab7;origin=https://github.com/JulienLie/Pyttern;visit=swh:1:snp:c54ff158a8b508cf8dcae4e9563f6a1bc2284ed9;anchor=swh:1:rev:5433d0be3b122cbff3e326b6312f128894dbe431}{\texttt{swh:1:dir:1e349725fe7d18d7785112e0599682d5e7508ab7}} (visited on 2025-09-17)},
   url = {https://github.com/JulienLie/Pyttern},
   doi = {10.4230/artifacts.24594},
}
Document
DOI: 10.4230/OASIcs.Programming.2025.21
A Comparison of Three Program Query Languages to Detect Python Programming Misconceptions

Authors: Quentin Colla, Kim Mens, and Julien Liénard

Published in: OASIcs, Volume 134, Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)

Abstract
Detecting and addressing common misconceptions in beginner programmers' code is key to improve their learning experience. This paper evaluates the effectiveness of three static program query languages and tools: Flake8, Regex and CodeQL, for identifying such misconceptions in Python code. We implemented a set of 20 common misconceptions using each language and compared them on a variety of criteria, including accuracy, performance, expressiveness, learning curve and query readability. Our analysis highlights strengths and limitations of each approach, providing insights into the most effective method for detecting programming misconceptions and enhancing feedback quality for learners.
Cite as

Quentin Colla, Kim Mens, and Julien Liénard. A Comparison of Three Program Query Languages to Detect Python Programming Misconceptions. In Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025). Open Access Series in Informatics (OASIcs), Volume 134, pp. 21:1-21:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{colla_et_al:OASIcs.Programming.2025.21,
  author =	{Colla, Quentin and Mens, Kim and Li\'{e}nard, Julien},
  title =	{{A Comparison of Three Program Query Languages to Detect Python Programming Misconceptions}},
  booktitle =	{Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)},
  pages =	{21:1--21:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-382-9},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{134},
  editor =	{Edwards, Jonathan and Perera, Roly and Petricek, Tomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Programming.2025.21},
  URN =		{urn:nbn:de:0030-drops-243052},
  doi =		{10.4230/OASIcs.Programming.2025.21},
  annote =	{Keywords: Static Program Analysis, Program Query Language, Python Programming, Programming Misconceptions}
}
Document
DOI: 10.4230/OASIcs.Programming.2025.23
The Pyttern Program Query Language

Authors: Julien Liénard, Kim Mens, and Siegfried Nijssen

Published in: OASIcs, Volume 134, Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)

Abstract
Despite the availability of numerous tools and languages for detecting structural patterns in programs, their complexity often presents a steep learning curve. This highlights the need for a program query language that is easier to learn, use, and read while remaining sufficiently expressive for defining and detecting relevant structural coding patterns in program code. To address this challenge, we present Pyttern, a query language that extends Python syntax with regular-expression-inspired wildcards, enabling intuitive pattern-based querying of Python code. Its implementation relies upon a custom pushdown automaton describing how to match patterns over program parse trees, thus providing a robust foundation for structural code analysis. We evaluate Pyttern’s usability and effectiveness through a study involving 35 master’s students, who were asked to write seven different patterns to identify known programming misconceptions. The results demonstrate that Pyttern is both easy to learn and practical to use, at least for analysing small-scale programs.
Cite as

Julien Liénard, Kim Mens, and Siegfried Nijssen. The Pyttern Program Query Language. In Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025). Open Access Series in Informatics (OASIcs), Volume 134, pp. 23:1-23:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{lienard_et_al:OASIcs.Programming.2025.23,
  author =	{Li\'{e}nard, Julien and Mens, Kim and Nijssen, Siegfried},
  title =	{{The Pyttern Program Query Language}},
  booktitle =	{Companion Proceedings of the 9th International Conference on the Art, Science, and Engineering of Programming (Programming 2025)},
  pages =	{23:1--23:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-382-9},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{134},
  editor =	{Edwards, Jonathan and Perera, Roly and Petricek, Tomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Programming.2025.23},
  URN =		{urn:nbn:de:0030-drops-243075},
  doi =		{10.4230/OASIcs.Programming.2025.23},
  annote =	{Keywords: Pyttern, Program Query Languages, Python, Pattern Matching, Parse Tree, Pushdown Automaton, Static Code Analysis, Wildcards, Tree Pattern Matching}
}
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