29 citations · 72 across the 8 of their papers we have counts for
8 papers
The Green Software Landscape: A Systematic Mapping Study on Evolution, Applications, Software Lifecycle, and Best Practices
Max Hort, Maria Kechagia, Federica Sarro
Energy consumption and climate change have made sustainability critical in Software Engineering (SE), driving the emergence of Green SE. Over the past 15 years, numerous solutions…
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…
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…
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…
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…
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…
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…
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…