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Documents authored by Tsouros, Dimos

Artifact
Software
DOI: 10.4230/artifacts.22502
CP-LLMs-ICL

Authors: Kostis Michailidis, Dimos Tsouros, and Tias Guns

Abstract
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Kostis Michailidis, Dimos Tsouros, Tias Guns. CP-LLMs-ICL (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@misc{dagstuhl-artifact-22502,
   title = {{CP-LLMs-ICL}}, 
   author = {Michailidis, Kostis and Tsouros, Dimos and Guns, Tias},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:5e4383ad6c4329796c9f21c51bbff4882dca8271}{\texttt{swh:1:dir:5e4383ad6c4329796c9f21c51bbff4882dca8271}} (visited on 2024-11-28)},
   url = {https://github.com/kostis-init/CP-LLMs-ICL},
   doi = {10.4230/artifacts.22502},
}
Document
DOI: 10.4230/LIPIcs.CP.2024.20
Constraint Modelling with LLMs Using In-Context Learning

Authors: Kostis Michailidis, Dimos Tsouros, and Tias Guns

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Constraint Programming (CP) allows for the modelling and solving of a wide range of combinatorial problems. However, modelling such problems using constraints over decision variables still requires significant expertise, both in conceptual thinking and syntactic use of modelling languages. In this work, we explore the potential of using pre-trained Large Language Models (LLMs) as coding assistants, to transform textual problem descriptions into concrete and executable CP specifications. We present different transformation pipelines with explicit intermediate representations, and we investigate the potential benefit of various retrieval-augmented example selection strategies for in-context learning. We evaluate our approach on 2 datasets from the literature, namely NL4Opt (optimisation) and Logic Grid Puzzles (satisfaction), and a heterogeneous set of exercises from a CP course. The results show that pre-trained LLMs have promising potential for initialising the modelling process, with retrieval-augmented in-context learning significantly enhancing their modelling capabilities.
Cite as

Kostis Michailidis, Dimos Tsouros, and Tias Guns. Constraint Modelling with LLMs Using In-Context Learning. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 20:1-20:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{michailidis_et_al:LIPIcs.CP.2024.20,
  author =	{Michailidis, Kostis and Tsouros, Dimos and Guns, Tias},
  title =	{{Constraint Modelling with LLMs Using In-Context Learning}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{20:1--20:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.20},
  URN =		{urn:nbn:de:0030-drops-207053},
  doi =		{10.4230/LIPIcs.CP.2024.20},
  annote =	{Keywords: Constraint Modelling, Constraint Acquisition, Constraint Programming, Large Language Models, In-Context Learning, Natural Language Processing, Named Entity Recognition, Retrieval-Augmented Generation, Optimisation}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.29
Mutational Fuzz Testing for Constraint Modeling Systems

Authors: Wout Vanroose, Ignace Bleukx, Jo Devriendt, Dimos Tsouros, Hélène Verhaeghe, and Tias Guns

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Constraint programming (CP) modeling languages, like MiniZinc, Essence and CPMpy, play a crucial role in making CP technology accessible to non-experts. Both solver-independent modeling frameworks and solvers themselves are complex pieces of software that can contain bugs, which undermines their usefulness. Mutational fuzz testing is a way to test complex systems by stochastically mutating input and verifying preserved properties of the mutated output. We investigate different mutations and verification methods that can be used on the constraint specifications directly. This includes methods proposed in the context of SMT problem specifications, as well as new methods related to global constraints, optimization, and solution counting/preservation. Our results show that such a fuzz testing approach improves the overall code coverage of a modeling system compared to only unit testing, and is able to find bugs in the whole toolchain, from the modeling language transformations themselves to the underlying solvers.
Cite as

Wout Vanroose, Ignace Bleukx, Jo Devriendt, Dimos Tsouros, Hélène Verhaeghe, and Tias Guns. Mutational Fuzz Testing for Constraint Modeling Systems. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 29:1-29:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{vanroose_et_al:LIPIcs.CP.2024.29,
  author =	{Vanroose, Wout and Bleukx, Ignace and Devriendt, Jo and Tsouros, Dimos and Verhaeghe, H\'{e}l\`{e}ne and Guns, Tias},
  title =	{{Mutational Fuzz Testing for Constraint Modeling Systems}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{29:1--29:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.29},
  URN =		{urn:nbn:de:0030-drops-207149},
  doi =		{10.4230/LIPIcs.CP.2024.29},
  annote =	{Keywords: fuzz testing, Constraint modeling language, bugs, mutational testing, modeling, constraint reformulation}
}
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