Exploring Multidomain Applications of Large Language Models in Software Engineering

Exploring Multidomain Applications of Large Language Models in Software Engineering

This project meets the demand for enhanced approaches by harnessing LLMs to elevate software engineering practices in specific research domains.

Large Language Models (LLMs) embark on a new era of artificial intelligence and will reshape our approach to Software Engineering practices. This master thesis project aims to revolutionise software systems design, development, testing, and operation using LLMs. With their natural language processing and generation capabilities, LLMs promise to automate and enhance software system engineering processes across many areas, such as self-driving car development and testing, digital twins' development, automating the design of quantum software, and supporting automated software system testing. This project addresses the need for more efficient and comprehensive methods and focuses on applying LLMs to specific research areas within software engineering. As a potential master's student, you can pick the domain that interests you most. Whether it is enhancing self-driving cars, exploring digital twins for cyber-physical systems, automating quantum software development, or ensuring software reliability, this project aligns with your preferences and aspirations. Each chosen domain presents a unique set of challenges and opportunities, and your work will contribute to a collective understanding of how LLMs can transform these fields.


Our primary goals are to harness the power of LLMs to streamline software engineering processes and drive innovation across diverse areas within software engineering in multiple domains.

Learning outcome

  • Profound grasp of LLMs' capabilities
  • Practical application experience
  • Innovative approach development
  • Domain-specific expertise


  • Research interest
  • Programming skills
  • Familiarity with ML and NLP concepts applicable to the domain of your choice


The supervisors assigned to the project will be considered based on the chosen thesis topic/domain.

  • Shaukat Ali
  • Erblin Isaku
  • Hassan Sartaj
  • Eñaut Mendiluze
  • Chengjie Lu

Associated contacts

Shaukat Ali

Head of DepartmentChief Research Scientist/Research Professor

Hassan Sartaj

Postdoctoral Fellow

Erblin Isaku

PhD student

Chengjie Lu

PhD student

Eñaut Mendiluze

PhD student