5 papers
Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair
David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez
This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs). Current APR systems a…
Analysis of instruction-based LLMs' capabilities to score and judge text-input problems in an academic setting
Valeria Ramirez-Garcia, David de-Fitero-Dominguez, Antonio Garcia-Cabot +1
Large language models (LLMs) can act as evaluators, a role studied by methods like LLM-as-a-Judge and fine-tuned judging LLMs. In the field of education, LLMs have been studied as…
RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment
Marcos Fuster-Pena, David de-Fitero-Dominguez, Antonio Garcia-Cabot +1
Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention. However, existing tools tend to generate patches that satisfy test…
Enhanced Automated Code Vulnerability Repair using Large Language Models
David de-Fitero-Dominguez, Eva Garcia-Lopez, Antonio Garcia-Cabot +1
This research addresses the complex challenge of automated repair of code vulnerabilities, vital for enhancing digital security in an increasingly technology-driven world. The stud…
Evaluating Large Language Models for automatic analysis of teacher simulations
David de-Fitero-Dominguez, Mariano Albaladejo-González, Mariano Albaladejo-González +5
Digital Simulations (DS) provide safe environments where users interact with an agent through conversational prompts, providing engaging learning experiences that can be used to tr…