1 citations · 1 across the 2 of their papers we have counts for
7 papers
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…
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…
DynamicsLLM: a Dynamic Analysis-based Tool for Generating Intelligent Execution Traces Using LLMs to Detect Android Behavioural Code Smells
Houcine Abdelkader Cherief, Florent Avellaneda, Naouel Moha
Mobile apps have become essential of our daily lives, making code quality a critical concern for developers. Behavioural code smells are characteristics in the source code that ind…
MLmisFinder: A Specification and Detection Approach of Machine Learning Service Misuses
Hadil Ben Amor, Niruthiha Selvanayagam, Manel Abdellatif +2
Machine Learning (ML) cloud services, offered by leading providers such as Amazon, Google, and Microsoft, enable the integration of ML components into software systems without buil…
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…
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…