Presenter: Dr. Mohammad Mahdi Hassan, candidate for a tenure-track position, UPEI School of Mathematical and Computational Sciences

Teaching talk: Wednesday, April 24, 9:00--10:00 am, Cass Science Hall 101

Research talk: Wednesday, April 24, 10:25--11:25 am, Cass Science Hall 101

Title: "Empirical Study on the Application of Quantum, LLM, and Federated Machine Learning in Software Engineering--Challenges and Opportunities!"

In this series of research endeavours, we explore the application of cutting-edge technologies in the domain of software engineering, poised to revolutionize traditional methodologies and enhance analytics. Our first study delves into the practical utilization of Quantum Support Vector Classifiers (QSVC) for detecting buggy software commits, leveraging the power of quantum computing to tackle complex challenges. Despite the computational hurdles posed by large datasets, our findings showcase the potential of quantum-assisted algorithms to significantly improve detection accuracy. Moving forward, we introduce an automated approach to deriving UML sequence diagrams from user stories, harnessing the capabilities of generative AI models to streamline design validation and communication processes in Agile development environments. By leveraging state-of-the-art techniques, our research illuminates novel pathways for enhancing collaboration and system understanding. Finally, we delve into analytics with a focus on Just-In-Time (JIT) and Real-Time (RT) bug-inducing commit prediction using Federated Learning (FL), a cutting-edge approach that addresses privacy concerns while maintaining robust model performance across diverse software projects. Through the application of advanced technologies, our studies pave the way for transformative advancements in software engineering, offering insights that stand at the forefront of innovation and industry relevance.

Teaching and research talks are open to the UPEI campus community.