6 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…
A Benchmark Framework for Screening Automation in Systematic Reviews
Gauransh Kumar, Luciano Marchezan, Guillaume Genois +2
Systematic reviews (SR) are essential for evidence-based research, but their screening phase is highly time-consuming and labor-intensive. Large language models (LLMs) offer a prom…
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,…
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.…