An Empirical Study of Challenges in Converting Deep Learning Models. - Moses Openja; Amin Nikanjam; Ahmed Haj Yahmed; Foutse Khomh; Zhen Ming Jack Jiang (ICSME, 2022)

DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment. - Ahmed Haj Yahmed; Houssem Ben Braiek; Foutse Khomh; Sonia Bouzidi; Rania Zaatour (Empir. Softw. Eng., 2022)

Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges. - Ahmed Haj Yahmed; Altaf Allah Abbassi; Amin Nikanjam; Heng Li 007; Foutse Khomh (ICSME, 2023)

An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems. - Ahmed Haj Yahmed; Rached Bouchoucha; Houssem Ben Braiek; Foutse Khomh (ASE, 2023)

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer. - Rached Bouchoucha; Ahmed Haj Yahmed; Darshan Patil; Janarthanan Rajendran; Amin Nikanjam; Sarath Chandar; Foutse Khomh (ICSME, 2024)

Towards Assessing Deep Learning Test Input Generators. - Seif Mzoughi; Ahmed Haj Yahmed; Mohamed Elshafei; Foutse Khomh; Diego Elias Costa (CoRR, 2025)