4 citations · 4 across the 6 of their papers we have counts for
6 papers
Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair
Fernando Vallecillos-Ruiz, Giordano d'Aloisio, Max Hort +3
Large Language Models (LLMs) are powerful tools and have been increasingly adopted for complex software engineering tasks. As the number of parameters increases, results can often…
When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
Sushant Gautam, Finn Schwall, Annika Willoch Olstad +6
Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this sett…
Wisdom and Delusion of LLM Ensembles for Code Generation and Repair
Fernando Vallecillos-Ruiz, Max Hort, Leon Moonen
Today's pursuit of a single Large Language Model (LMM) for all software engineering tasks is resource-intensive and overlooks the potential benefits of complementarity, where diffe…
The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models
Fernando Vallecillos Ruiz, Max Hort, Leon Moonen
Automatic program repair (APR) aims to reduce the manual efforts required to identify and fix errors in source code. Before the rise of LLM-based agents, a common strategy was to i…
Agent-Driven Automatic Software Improvement
Fernando Vallecillos Ruiz
With software maintenance accounting for 50% of the cost of developing software, enhancing code quality and reliability has become more critical than ever. In response to this chal…
Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation
Fernando Vallecillos Ruiz, Anastasiia Grishina, Max Hort +1
Research shows that errors in natural language can be corrected by translating texts to another language and back using language models. We explore to what extent this latent corre…