1 citations · 1 across the 3 of their papers we have counts for
4 papers
RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated Code
Zacharie Chenail-Larcher, Daniele Lunghi, Melissa Tessa +5
Evaluating approaches that improve the security of LLM code generation requires datasets that jointly assess security and functionality and remain adapted to the capabilities of th…
LLM Code Smells: A Taxonomy and Detection Approach
Zacharie Chenail-Larcher, Brahim Mahmoudi, Naouel Moha +2
Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning…
GLiSE: A Prompt-Driven and ML-Powered Tool for Automated Grey Literature Extraction in Software Engineering
Houcine Abdelkader Cherief, Brahim Mahmoudi, Zacharie Chenail-Larcher +3
Grey literature is essential to software engineering research as it captures practices and decisions that rarely appear in academic venues. However, collecting and assessing it at…
SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs
Brahim Mahmoudi, Naouel Moha, Quentin Stiévenart +1
Machine Learning (ML) pipelines encode quality-relevant decisions across data preparation, training, evaluation, and configuration code. Some recurring source-level quality problem…