76 citations · 278 across the 39 of their papers we have counts for
41 papers
Does AI-Assisted Development Homogenize Software? An Empirical Study of Code Similarity, Quality, and Evolution
Luciano Marchezan, Romain Lefeuvre, Kevin Delcourt +2
AI-assisted software development is evolving from interactive LLM assistance toward agentic systems that autonomously perform software engineering tasks, yet the effects of this sh…
Type-IV Code Clone Detection via Layer-Wise Non-Contrastive Representation Learning
Luciano Marchezan, Kevin Delcourt, Eugene Syriani +1
Software clones are fragments of code that are similar or functionally equivalent to each other. They pose significant challenges for maintenance, refactoring, and bug detection. D…
Breaking Models to Test the Judge: A Mutation Testing Approach for Semantic Evaluators of Domain Class Diagrams
Kevin Delcourt, Meriem Ben Chaaben, Abdelhamid Rouatbi +2
In software engineering, many semantic modeling tasks lack a unique ground truth, as human judgments are both costly and subjective. This paper explores mutation testing as a scala…
Towards Automated Domain Model Extraction from Source Code using Heuristics and Open-Source LLMs
Alessandra Mancas, Mounir Ammam, Hyacinth Ali +2
Large language models (LLMs) have recently shown strong capabilities for code understanding, making them promising for reverse engineering domain models from source code. However,…
Balancing Usefulness and Naturalness: An LLM-based Curation Pipeline for Code Review Comments
Oussama Ben Sghaier, Martin Weyssow, Houari Sahraoui
Code review is a cornerstone of software development, where reviewers provide feedback through written comments to ensure code quality, maintainability, and correctness. The effect…
Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems
Pascal Archambault, Houari Sahraoui, Eugene Syriani
Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled by mechanistic simulators w…
From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
Hyacinth Ali, Jessie Galasso-Carbonnel, Houari Sahraoui
Artificial Intelligence (AI) pipelines have become integral to modern research, supporting fields such as Medical Sciences, Agriculture, and Social Sciences, and enabling large-sca…
Modeling Sampling Workflows for Code Repositories
Romain Lefeuvre, Maïwenn Le Goasteller, Jessie Galasso +3
Empirical software engineering research often depends on datasets of code repository artifacts, where sampling strategies are employed to enable large-scale analyses. The design an…
Artificial or Just Artful? Do LLMs Bend the Rules in Programming?
Oussama Ben Sghaier, Kevin Delcourt, Houari Sahraoui
Large Language Models (LLMs) are widely used for automated code generation, yet their apparent successes often mask a tension between pretraining objectives and alignment choices.…
Leveraging Reward Models for Guiding Code Review Comment Generation
Oussama Ben Sghaier, Rosalia Tufano, Gabriele Bavota +1
Code review is a crucial component of modern software development, involving the evaluation of code quality, providing feedback on potential issues, and refining the code to addres…
MONO2REST: Identifying and Exposing Microservices: a Reusable RESTification Approach
Matthéo Lecrivain, Hanifa Barry, Dalila Tamzalit +1
The microservices architectural style has become the de facto standard for large-scale cloud applications, offering numerous benefits in scalability, maintainability, and deploymen…
Combining Large Language Models with Static Analyzers for Code Review Generation
Imen Jaoua, Oussama Ben Sghaier, Houari Sahraoui
Code review is a crucial but often complex, subjective, and time-consuming activity in software development. Over the past decades, significant efforts have been made to automate t…
Harnessing Large Language Models for Curated Code Reviews
Oussama Ben Sghaier, Martin Weyssow, Houari Sahraoui
In code review, generating structured and relevant comments is crucial for identifying code issues and facilitating accurate code changes that ensure an efficient code review proce…
On the Utility of Domain Modeling Assistance with Large Language Models
Meriem Ben Chaaben, Lola Burgueño, Istvan David +1
Model-driven engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to synta…
Automation in Model-Driven Engineering: A look back, and ahead
Lola Burgueño, Davide Di Ruscio, Houari Sahraoui +1
Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to impleme…
CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences
Martin Weyssow, Aton Kamanda, Xin Zhou +1
Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavour that requires a deep assessment of LLMs' outputs. Existing…
Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge Distillation
Oussama Ben Sghaier, Houari Sahraoui
Code review is a fundamental process in software development that plays a pivotal role in ensuring code quality and reducing the likelihood of errors and bugs. However, code review…
CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code
Martin Weyssow, Claudio Di Sipio, Davide Di Ruscio +1
Motivated by recent work on lifelong learning applications for language models (LMs) of code, we introduce CodeLL, a lifelong learning dataset focused on code changes. Our contribu…
Toward Optimal Psychological Functioning in AI-driven Software Engineering Tasks: The SEWELL-CARE Assessment Framework
Oussama Ben Sghaier, Jean-Sebastien Boudrias, Houari Sahraoui
In the field of software engineering, there has been a shift towards utilizing various artificial intelligence techniques to address challenges and create innovative tools. These s…
Unity is Strength: Cross-Task Knowledge Distillation to Improve Code Review Generation
Oussama Ben Sghaier, Lucas Maes, Houari Sahraoui
Code review is a fundamental process in software development that plays a critical role in ensuring code quality and reducing the likelihood of errors and bugs. However, code revie…
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
Martin Weyssow, Xin Zhou, Kisub Kim +2
Large language models (LLMs) demonstrate impressive capabilities to generate accurate code snippets given natural language intents in a zero-shot manner, i.e., without the need for…
Fingerprinting and Building Large Reproducible Datasets
Romain Lefeuvre, Jessie Galasso, Benoit Combemale +2
Obtaining a relevant dataset is central to conducting empirical studies in software engineering. However, in the context of mining software repositories, the lack of appropriate to…
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
Martin Weyssow, Xin Zhou, Kisub Kim +2
Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general know…
Towards using Few-Shot Prompt Learning for Automating Model Completion
Meriem Ben Chaaben, Lola Burgueño, Houari Sahraoui
We propose a simple yet a novel approach to improve completion in domain modeling activities. Our approach exploits the power of large language models by using few-shot prompt lear…