8 citations · 9 across the 5 of their papers we have counts for
7 papers
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 Impact of Fine-tuning Large Language Models on Automated Program Repair
Roman Macháček, Anastasiia Grishina, Max Hort +1
Automated Program Repair (APR) uses various tools and techniques to help developers achieve functional and error-free code faster. In recent years, Large Language Models (LLMs) hav…
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…
Codehacks: A Dataset of Adversarial Tests for Competitive Programming Problems Obtained from Codeforces
Max Hort, Leon Moonen
Software is used in critical applications in our day-to-day life and it is important to ensure its correctness. One popular approach to assess correctness is to evaluate software o…
Semantic-Preserving Transformations as Mutation Operators: A Study on Their Effectiveness in Defect Detection
Max Hort, Linas Vidziunas, Leon Moonen
Recent advances in defect detection use language models. Existing works enhanced the training data to improve the models' robustness when applied to semantically identical code (i.…
Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models
Anastasiia Grishina, Vadim Liventsev, Aki Härmä +1
Program synthesis with Large Language Models (LLMs) suffers from a "near-miss syndrome": the generated code closely resembles a correct solution but fails unit tests due to minor e…