Models & Languages Lab

Selected publications

1. ASE

   AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models

   López, José Antonio Hernández, Weyssow, Martin, Cuadrado, Jesús Sánchez, and Sahraoui, Houari

   In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering 2022

   Bib PDF

   @inproceedings{lopez2022ase,
     title = {AST-Probe: Recovering abstract syntax trees from hidden
     representations of pre-trained language models},
     author = {L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and Weyssow, Martin and Cuadrado, Cuadrado, Jes{\'u}s S{\'a}nchez and Sahraoui, Houari},
     booktitle = {Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering},
     year = {2022},
     bibtex_show = {true},
     abbr = {ASE},
     pdf = {https://arxiv.org/pdf/2206.11719.pdf},
     selected = {true}
   }

2. 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}
   }

3. 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}
   }

4. 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}
   }

5. 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}
   }