26 citations · 29 across the 10 of their papers we have counts for
12 papers
A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agents
Wuyang Dai, Moses Openja, Jiho Shin +2
Large language model (LLM)-based agents are increasingly used across software engineering, web automation, research, and productivity applications. Their integration of planning, m…
ABTest: Behavior-Driven Testing for AI Coding Agents
Wuyang Dai, Moses Openja, Hung Viet Pham +3
AI coding agents are increasingly integrated into real-world software development workflows, yet their robustness under diverse and adversarial scenarios remains poorly understood.…
Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models
Gianmario Voria, Moses Openja, Foutse Khomh +2
The advent of transformer-based language models has reshaped how AI systems process and generate text. In software engineering (SE), these models now support diverse activities, ac…
FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
Moses Openja, Paolo Arcaini, Foutse Khomh +1
Deep neural networks (DNNs) are being utilized in various aspects of our daily lives, including high-stakes decision-making applications that impact individuals. However, these sys…
An empirical study of testing machine learning in the wild
Moses Openja, Foutse Khomh, Armstrong Foundjem +4
Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems…
Detection and Evaluation of bias-inducing Features in Machine learning
Moses Openja, Gabriel Laberge, Foutse Khomh
The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This impli…
An Empirical Study on the Usage of Automated Machine Learning Tools
Forough Majidi, Moses Openja, Foutse Khomh +1
The popularity of automated machine learning (AutoML) tools in different domains has increased over the past few years. Machine learning (ML) practitioners use AutoML tools to auto…
An Empirical Study of Challenges in Converting Deep Learning Models
Moses Openja, Amin Nikanjam, Ahmed Haj Yahmed +3
There is an increase in deploying Deep Learning (DL)-based software systems in real-world applications. Usually DL models are developed and trained using DL frameworks that have th…
Studying the Practices of Deploying Machine Learning Projects on Docker
Moses Openja, Forough Majidi, Foutse Khomh +2
Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code. St…
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…
Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues
Mohamed Raed El aoun, Heng Li, Foutse Khomh +1
With the advance in quantum computing, quantum software becomes critical for exploring the full potential of quantum computing systems. Recently, quantum software engineering (QSE)…
Are Multi-language Design Smells Fault-prone? An Empirical Study
Mouna Abidi, Md Saidur Rahman, Moses Openja +1
Nowadays, modern applications are developed using components written in different programming languages. These systems introduce several advantages. However, as the number of langu…