Dr Muhammad Sheraz Anjum is currently a SyMeCo postdoctoral fellow at with Lero@UL, and is undertaking his fellowship under the supervision of Prof Conor Ryan. Sheraz completed his Ph.D. in Computer Science with a focus on evolutionary testing and genetic algorithms, and holds an MS in Information Technology from the National University of Sciences and Technology (NUST), Pakistan. His research interests span software testing, evolutionary computation, relational databases, and big data analytics. He has taught a wide range of undergraduate and postgraduate computer science courses and has been actively involved in curriculum design and quality enhancement processes. Prior to joining the University of Limerick, he served as an Assistant Professor at the American University of Sharjah (UAE) and at Namal Institute (Pakistan). He is passionate about advancing AI-driven techniques to improve software reliability and test automation, and continues to contribute to the academic research community through his work in software engineering and applied computing.

Sheraz’s SyMeCo research project, titled “Extending the Limits of Ariadne, the Grammatical Evolution (GE)-Based Test Data Generator”, aims to advance automated software test data generation using Grammatical Evolution (GE) by enhancing the Ariadne framework. The fellowship focuses on improving boundary value coverage through grammar-based test generation, enriching GE grammars with domain knowledge and mathematical operators to satisfy complex program conditions, and developing a pure GE-based system for evolving test programs for object-oriented software. Overall, the project seeks to improve the effectiveness and expressiveness of evolutionary techniques for automated software testing.

Project impact – The expected impact of this project is the advancement of automated software testing through a more effective and scalable alternative to conventional GA-based software testing approaches. By extending the GE-based framework to support boundary value coverage, complex variable and mathematical interdependencies, and LLM-generated code, the project will enable more precise and reliable test generation for real-world software systems. This will improve fault detection, reduce manual testing effort and associated costs, and enhance software reliability in both industrial and emerging AI-assisted development contexts, while also establishing Grammatical Evolution as a stronger and more versatile foundation for future research and practice in evolutionary testing.