6 papers
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…
Automating the Detection of Requirement Dependencies Using Large Language Models
Ikram Darif, Feifei Niu, Manel Abdellatif +3
Requirements are inherently interconnected through various types of dependencies. Identifying these dependencies is essential, as they underpin critical decisions and influence a r…
Self-Admitted Technical Debt in LLM Software: An Empirical Comparison with ML and Non-ML Software
Niruthiha Selvanayagam, Taher A. Ghaleb, Manel Abdellatif
Self-admitted technical debt (SATD), referring to comments flagged by developers that explicitly acknowledge suboptimal code or incomplete functionality, has received extensive att…
A Comprehensive Multi-Vocal Empirical Study of ML Cloud Service Misuses
Hadil Ben Amor, Manel Abdellatif, Taher Ghaleb
Machine Learning (ML) models are widely used across various domains, including medical diagnostics and autonomous driving. To support this growth, cloud providers offer ML services…
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks for Image Analysis
Zohreh Aghababaeyan, Manel Abdellatif, Lionel Briand +1
Deep Neural Networks (DNNs) are increasingly deployed across applications. However, ensuring their reliability remains a challenge, and in many situations, alternative models with…
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
Amirhossein Zolfagharian, Manel Abdellatif, Lionel C. Briand +1
Deep Reinforcement Learning (DRL) has made significant advancements in various fields, such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal pol…