8 citations · 15 across the 13 of their papers we have counts for
14 papers
Memetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks
Birk Torpmann-Hagen, Finn Schwall, Leon Moonen
Autonomous large language model (LLM) agents increasingly interact in network environments where adversarial content can propagate between agents. Known attacks include agent worms…
Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry
Jakob Solberg Berntzen, Safia Fatima, Leon Moonen
Low-cost unmanned ground vehicles are often used in indoor places like warehouses, inspection corridors, and farm rows, where painted floor lines guide the robot. Line following is…
CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
Safia Fatima, Kai Olav Ellefsen, Leon Moonen
Traditional reinforcement learning (RL) for recovery in autonomous systems lacks causal understanding and generalizes poorly to novel failure scenarios. RL policies often stall in…
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 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…
A Comparative Study on Large Language Models for Log Parsing
Merve Astekin, Max Hort, Leon Moonen
Background: Log messages provide valuable information about the status of software systems. This information is provided in an unstructured fashion and automated approaches are app…
The Impact of Program Reduction on Automated Program Repair
Linas Vidziunas, David Binkley, Leon Moonen
Correcting bugs using modern Automated Program Repair (APR) can be both time-consuming and resource-expensive. We describe a program repair approach that aims to improve the scalab…
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…