Effective issue resolution is crucial for maintaining software quality. Yet developers frequently encounter challenges such as low-quality issue reports, limited understanding of real-world workflows, and a lack of automated support. This research aims to address these challenges through three complementary directions. First, we enhance issue report quality by proposing techniques that leverage LLM reasoning and application-specific information. Second, we empirically characterize developer workflows in both traditional and AI-augmented systems. Third, we automate cognitively demanding resolution tasks, including buggy UI localization and solution identification, through ML, DL, and LLM-based approaches. Together, our work delivers empirical insights, practical tools, and automated methods to advance AI-driven issue resolution, supporting more maintainable and high-quality software systems.

I am a Ph.D. Candidate at Department of Computer Science at William & Mary. I am working as a Research Assistant of Dr. Oscar Chaparro in the Software Evolution and Analysis (SEA) Lab. My research interest includes software engineering, software maintenance and evolution, bug triage and resolution, and deep learning for software engineering. Currently, I am leading four research projects including investigating issue resolution patterns, identifying solution from developers discussion, buggy UI localization, and bug report assessment & improvement using LLMs.

Besides this, I am a Lecturer in the Department of Computer Science and Engineering (CSE) at Dhaka University of Engineering & Technology (DUET), Gazipur, Bangladesh. Currently, I am on a study leave from DUET for pursuing my Ph.D. degree. Prior to this I worked as a Lecturer in the Department of Computer Science and Engineering (CSE) at Military Institute of Science and Technology (MIST), Bangladesh. I graduated with a B.Sc. degree in Computer Science & Engineering (CSE) from Rajshahi University of Engineering & Technology (RUET), Rajshahi, Bangladesh.