Models & Languages Lab

All models are wrong, but some are useful.

We are a (small) research team focused on Software Engineering topics. In particular, we seek to improve software development by using models (as in UML models, but also ML models) and Domain-Specific Languages (DSLs).

We have three main themes.

Model transformations
Domain-specific languages
Machine learning applied to modelling

Our work revolves around these topics. We like to devise practical solutions to problems related to software engineering, usually by building tools. If you are interested in these topics and want to collaborate or work with us, do not hesitate to contact us.

News

Oct 27, 2024	We’ve been awarded with the Best Paper Award at MODELS’25 for ModelMate: A recommender for textual modeling languages based on pre-trained language models
Sep 27, 2024	Our work ModelMate: A recommender for textual modeling languages based on pre-trained language models will be presented at MODELS’24
Jul 9, 2023	Our work Word Embeddings for Model-Driven Engineering has been accepted at MODELS’23 and received the Best Paper Award
Dec 14, 2022	Our work about Generating structurally realistic models with deep autoregressive networks has been published at IEEE TSE
Oct 21, 2022	We will give three talks at MoDELS’22, all in the session “AI for/with MDE”. Checkout the conference program

Selected publications

MoDELS

ModelMate: A recommender for textual modeling languages based on pre-trained language models

Carlos Durá, José Antonio Hernández, and Cuadrado, Jesús Sánchez

In Proceedings of the 27th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems 2024

Bib PDF

@inproceedings{dura2024modelmate,
  title = {ModelMate: A recommender for textual modeling languages based on pre-trained language models},
  author = {Carlos Dur\'a, L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and Cuadrado, Jes{\'u}s S{\'a}nchez},
  booktitle = {Proceedings of the 27th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems},
  year = {2024},
  bibtex_show = {true},
  abbr = {MoDELS},
  pdf = {https://jesusc.github.io/papers/models24-modelmate.pdf},
  selected = {true}
}

MoDELS

MAR: A structure-based search engine for models

López, José Antonio Hernández, and Cuadrado, Jesús Sánchez

In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems 2020

Bib HTML PDF

@inproceedings{lopez2020mar,
  title = {MAR: {A} structure-based search engine for models},
  author = {L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and Cuadrado, Cuadrado, Jes{\'u}s S{\'a}nchez},
  booktitle = {Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems},
  pages = {57--67},
  year = {2020},
  bibtex_show = {true},
  abbr = {MoDELS},
  pdf = {http://sanchezcuadrado.es/papers/models2020-mar.pdf},
  html = {https://dl.acm.org/doi/10.1145/3365438.3410947},
  selected = {true}
}

MoDELS
Towards the Characterization of Realistic Model Generators using Graph Neural Networks

López, José Antonio Hernández, and Cuadrado, Jesús Sánchez

In Proceedings of the 24th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems 2021

Abs Bib PDF

The automatic generation of software models is an important element in many software and systems engineering scenarios such as software tool certification, validation of cyber-physical systems, or benchmarking graph databases. Several model generators are nowadays available, but the topic of whether they generate realistic models has been little studied. The state-of-the-art approach to check the realistic property in software models is to rely on simple comparisons using graph metrics and statistics. This generates a bottleneck due to the compression of all the information contained in the model into a small set of metrics. Furthermore, there is a lack of interpretation in these approaches since there are no hints of why the generated models are not realistic. Therefore, in this paper, we tackle the problem of assessing how realistic a generator is by mapping it to a classification problem in which a Graph Neural Network (GNN) will be trained to distinguish between the two sets of models (real and synthetic ones). Then, to assess how realistic a generator is we perform the Classifier Two-Sample Test (C2ST). Our approach allows for interpretation of the results by inspecting the attention layer of the GNN. We use our approach to assess four state-of-the-art model generators applied to three different domains. The results show that none of the generators can be considered realistic.
@inproceedings{lopez2020gen, title = {{T}owards the {C}haracterization of {R}ealistic {M}odel {G}enerators using {G}raph {N}eural {N}etworks}, author = {L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and Cuadrado, Cuadrado, Jes{\'u}s S{\'a}nchez}, booktitle = {Proceedings of the 24th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems}, year = {2021}, bibtex_show = {true}, abbr = {MoDELS}, pdf = {http://sanchezcuadrado.es/papers/models21-realistic-model-generators.pdf}, selected = {true} }

TSE

Generating structurally realistic models with deep autoregressive networks

López, José Antonio Hernández, and Cuadrado, Jesús Sánchez

IEEE Transactions on Software Engineering 2022

Bib HTML PDF

@article{lopez2022realistic,
  title = {Generating structurally realistic models with deep autoregressive networks},
  author = {L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and Cuadrado, Cuadrado, Jes{\'u}s S{\'a}nchez},
  journal = {IEEE Transactions on Software Engineering},
  year = {2022},
  publisher = {IEEE},
  bibtex_show = {true},
  abbr = {TSE},
  pdf = {http://sanchezcuadrado.es/papers/Realistic-Models-TSE-2022.pdf},
  html = {https://ieeexplore.ieee.org/document/9982379},
  selected = {true}
}

TSE
Efficient execution of ATL model transformations using static analysis and parallelism

Cuadrado, Jesús Sánchez, Burgueno, Loli, Wimmer, Manuel, and Vallecillo, Antonio

IEEE Transactions on Software Engineering 2020

Bib HTML PDF
@article{cuadrado2020efficient, title = {Efficient execution of ATL model transformations using static analysis and parallelism}, author = {Cuadrado, Jes{\'u}s S{\'a}nchez and Burgueno, Loli and Wimmer, Manuel and Vallecillo, Antonio}, journal = {IEEE Transactions on Software Engineering}, year = {2020}, publisher = {IEEE}, bibtex_show = {true}, abbr = {TSE}, pdf = {http://sanchezcuadrado.es/papers/a2l.tse2020.pdf}, html = {https://ieeexplore.ieee.org/abstract/document/9146715}, selected = {true} }