Publications (15)
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.…
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
Anastasiia Grishina, Max Hort, Leon Moonen
The use of modern Natural Language Processing (NLP) techniques has shown to be beneficial for software engineering tasks, such as vulnerability detection and type inference. Howeve…
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…
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…
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort, Zhenpeng Chen, Jie M. Zhang +2
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias…
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…
FairRF: Multi-Objective Search for Single and Intersectional Software Fairness
Giordano d'Alosio, Max Hort, Rebecca Moussa +1
Background: The wide adoption of AI- and ML-based systems in sensitive domains raises severe concerns about their fairness. Many methods have been proposed in the literature to enh…
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
Minghua Ma, Zhao Tian, Max Hort +4
Fairness testing aims at mitigating unintended discrimination in the decision-making process of data-driven AI systems. Individual discrimination may occur when an AI model makes d…
Empirical and Sustainability Aspects of Software Engineering Research in the Era of Large Language Models: A Reflection
David Williams, Max Hort, Maria Kechagia +3
Software Engineering (SE) research involving the use of Large Language Models (LLMs) has introduced several new challenges related to rigour in benchmarking, contamination, replica…
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Zhenpeng Chen, Jie M. Zhang, Max Hort +2
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedica…
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…
An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code
Max Hort, Anastasiia Grishina, Leon Moonen
Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair. Large amounts of data for training…
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…
The Impact of Fine-tuning Large Language Models on Automated Program Repair
Roman MacháÄek, 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…
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…