11 citations · 33 across the 16 of their papers we have counts for
3 papers · 1 filter
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…