@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}}
MoDELS
Machine learning methods for model classification: A comparative study
López, José Antonio Hernández,
Rubei, Riccardo,
Cuadrado, Jesús Sánchez,
and Ruscio, Davide
In Proceedings of the 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems
2022
@inproceedings{lopez2022classification,title={Machine learning methods for model classification: A comparative study},author={L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and Rubei, Riccardo and Cuadrado, Cuadrado, Jes{\'u}s S{\'a}nchez and di Ruscio, Davide},booktitle={Proceedings of the 25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems},year={2022},bibtex_show={true},abbr={MoDELS},pdf={http://sanchezcuadrado.es/papers/models22-classification.pdf},selected={false}}
TSE
Generating structurally realistic models with deep autoregressive networks
López, José Antonio Hernández,
and Cuadrado, Jesús Sánchez
@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}}
2021
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
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}}
SoSyM
ModelSet: A Dataset for Machine Learning in Model-Driven Engineering
López, José Antonio Hernández,
Cánovas Izquierdo, JavierLuis,
and Cuadrado, Jesús Sánchez
@article{lopez2021modelset,title={{M}odel{S}et: {A} {D}ataset for {M}achine Learning in {M}odel-{D}riven {E}ngineering},author={L{\'o}pez, Jos{\'e} Antonio Hern{\'a}ndez and C\'anovas Izquierdo, JavierLuis and Cuadrado, Jes{\'u}s S{\'a}nchez},journal={Software and Systems Modeling},year={2021},publisher={Springer},bibtex_show={true},abbr={SoSyM},pdf={http://sanchezcuadrado.es/papers/modelset.pdf},html={https://link.springer.com/article/10.1008/s10270-019-00740-1}}
2020
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
@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}}
TSE
Efficient execution of ATL model transformations using static analysis and parallelism
Cuadrado, Jesús Sánchez,
Burgueno, Loli,
Wimmer, Manuel,
and Vallecillo, Antonio
@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}}
@article{cuadrado2020verified,title={A verified catalogue of OCL optimisations},author={Cuadrado, Jes{\'u}s S{\'a}nchez},journal={Software and Systems Modeling},volume={19},number={5},pages={1139--1161},year={2020},publisher={Springer},bibtex_show={true},abbr={SoSyM},pdf={http://sanchezcuadrado.es/papers/sosym-ocl-optimisations.pdf},html={https://link.springer.com/article/10.1007/s10270-019-00740-1}}
ECMFA
Model Finding in the EMF Ecosystem
Cuadrado, Jesús Sánchez,
and Gogolla, Martin
In Proceedings of the 16th European Conference on Modelling Foundations and Applications
2020