1 citations · 6 across the 11 of their papers we have counts for
11 papers
LLM Serving in the Wild: An Empirical Study of Frameworks, Methods, and System Designs
Forough Majidi, Mohammad Mehdi Morovati, Foutse Khomh +1
Large Language Models (LLMs) are integrated into software systems and AI services, making efficient LLM serving a concern for software engineering. Serving LLMs is challenging beca…
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
Iren Mazloomzadeh, Mohammad Mehdi Morovati, Foutse Khomh
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of mo…
Characterizing Faults in Agentic AI: A Taxonomy of Types, Symptoms, and Root Causes
Mehil B Shah, Mohammad Mehdi Morovati, Mohammad Masudur Rahman +1
Agentic AI systems combine LLM-based reasoning, orchestration, tool invocation, and interaction with external environments. These systems introduce faults that are difficult to cha…
Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy
Mina Taraghi, Mohammad Mehdi Morovati, Foutse Khomh
The rapid adoption of foundation models has significantly expanded the capabilities of software systems, enabling them to perform complex language, reasoning, and interaction tasks…
Fault Localization in Deep Learning-based Software: A System-level Approach
Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh
Over the past decade, Deep Learning (DL) has become an integral part of our daily lives. This surge in DL usage has heightened the need for developing reliable DL software systems.…
Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study
Mohammad Mehdi Morovati, Florian Tambon, Mina Taraghi +2
Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to produce intelligent agents. Despite rece…
Bug Characterization in Machine Learning-based Systems
Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon +3
Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component…
Bugs in Machine Learning-based Systems: A Faultload Benchmark
Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh +2
The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and…
Technical Debts and Faults in Open-source Quantum Software Systems: An Empirical Study
Moses Openja, Mohammad Mehdi Morovati, Le An +2
Quantum computing is a rapidly growing field attracting the interest of both researchers and software developers. Supported by its numerous open-source tools, developers can now bu…
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations
Amin Nikanjam, Houssem Ben Braiek, Mohammad Mehdi Morovati +1
Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the ne…
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach
Amin Nikanjam, Mohammad Mehdi Morovati, Foutse Khomh +1
A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is t…