Abdul Razzaq

1. Source code analysis 
2. Code visualization and interactions through knowledge graphs 
3. Software metrics and evaluation  
4. Developer Experience evaluation 
5. Natural Language Processing and Text Analytics using LLM (Text classification, summarization, prediction, sequence modelling, etc.) 
6. Research work that leads to development of a tool (in future) that ultimately helps the software developers in industry (e.g., code parsing or refactoring tool, developer activity context building tool, etc.) 
7. Standardization of the software/tools/dataset characteristics using software metrics 
8. Systematic Literature Reviews in Software Engineering Problems 

Dr. Abdul Razzaq is an Assistant Professor in Computer Science at the University of Limerick, specializing in AI-driven software engineering, developer experience, empirical software analytics, and cloud computing. His research focuses on enhancing developer productivity, AI-powered software refactoring, and the application of AI in education.

He holds a Ph.D. in Software Engineering from Lero – Science Foundation Ireland Research Centre for Software, where his research explored the empirical evaluation of feature location techniques in large-scale software systems. Prior to that, he earned an M.Sc. in Computer Science and a B.Sc. in Computer Science, developing a strong foundation in AI, software engineering, and empirical research methodologies.

With a strong track record in high-impact research, Dr. Razzaq has published in leading Q1 journals, including ACM TOSEM, IEEE TSE, and EMSE, accumulating 432 citations, an H-index of 10, and an i10-index of 12. His work has also translated into successful research commercialization, with AI-driven software refactoring tools licensed to Huawei and the first industry-adopted software engineering tool from Lero, JITTAC.

Dr. Razzaq is an active PhD and MSc supervisor, mentoring students in AI, NLP, cloud computing, and software engineering. He integrates progressive research scaffolding and guided autonomy in supervision, equipping students with independent research skills and real-world problem-solving capabilities. He is also engaged in curriculum development and pedagogical innovations, having led QQI revalidation for Data Analytics programs and integrated Universal Design for Learning (UDL) principles into module design.

Beyond academia, he actively contributes to industry-academic partnerships, collaborating with Huawei, ACI Worldwide, and GLOCC Group to bridge academic research with enterprise solutions. His public engagement efforts include contributions to the COVIGILANT digital contact tracing project, interdisciplinary hackathons, and policy discussions on AI and software engineering.

Dr. Razzaq is a member of ACM & IEEE, a reviewer for IEEE TSE, EMSE, and Wiley JSEP, and an advocate for AI-driven educational transformation. His ongoing research aims to expand AI’s role in software engineering, developer experience, and digital education, ensuring a broader academic, industry, and societal impact.

My teaching interests span across Artificial Intelligence, Software Engineering, and Cloud Computing, integrating hands-on learning, Universal Design for Learning (UDL) principles, and AI-driven pedagogical innovations.

Key Teaching Areas:

Artificial Intelligence & Machine Learning – Deep learning, NLP, generative AI, explainable AI, and AI applications in software engineering
Software Engineering – Software architecture, empirical software research, developer experience, and feature location techniques
Cloud Computing & Distributed Systems – Cloud infrastructures, containerization, microservices, and AI-driven cloud optimization
Programming & System Development – Event-driven programming, enterprise applications, and networked systems in Java, Python, and C#
Research Methods & Critical Thinking – Equipping students with structured research skills, publication strategies, and experimental methodologies