most citedSpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs

1 citations · 1 across the 2 of their papers we have counts for

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5 papers

cs.SE20261 cited

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2025

Specification and Detection of LLM Code Smells

Brahim Mahmoudi, Zacharie Chenail-Larcher, Naouel Moha +2

Large Language Models (LLMs) have gained massive popularity in recent years and are increasingly integrated into software systems for diverse purposes. However, poorly integrating…

cs.SE2025

A Systematic Literature Review of Machine Learning Approaches for Migrating Monolithic Systems to Microservices

Imen Trabelsi, Brahim Mahmoudi, Jean Baptiste Minani +2

Scalability and maintainability challenges in monolithic systems have led to the adoption of microservices, which divide systems into smaller, independent services. However, migrat…