5 papers
Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs
Francisco Sedeño, Francisco Chicano, Jamal Toutouh
Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unsta…
QuantumX: an experience for the consolidation of Quantum Computing and Quantum Software Engineering as an emerging discipline
Juan M. Murillo, Ignacio GarcÃa RodrÃguez de Guzmán, Enrique Moguel +19
The first edition of the QuantumX track, held within the XXIX Jornadas de IngenierÃa del Software y Bases de Datos (JISBD 2025), brought together leading Spanish research groups w…
Multi-objective Integer Linear Programming approach for Automatic Software Cognitive Complexity Reduction
Adriana Novoa-Hurtado, Rubén Saborido, Francisco Chicano +1
Clear and concise code is necessary to ensure maintainability, so it is crucial that the software is as simple as possible to understand, to avoid bugs and, above all, vulnerabilit…
Formal Methods Meets Readability: Auto-Documenting JML Java Code
Juan Carlos Recio Abad, Ruben Saborido, Francisco Chicano
This paper investigates whether formal specifications using Java Modeling Language (JML) can enhance the quality of Large Language Model (LLM)-generated Javadocs. While LLMs excel…
Generate more than one child in your co-evolutionary semi-supervised learning GAN
Francisco Sedeño, Jamal Toutouh, Francisco Chicano
Generative Adversarial Networks (GANs) are very useful methods to address semi-supervised learning (SSL) datasets, thanks to their ability to generate samples similar to real data.…