Fully Funded Scholarship University of Birmingham, Intelligent Software Engineering in the Era of Large Language Model

Software systems are eating the world but Artificial intelligence (AI) is eating the software systems. Given the tremendous development of AI in recent years, innovations like the Large Language Model (LLM), e.g., ChartGPT, have significantly impacted our daily lives. Indeed, LLM does not only change the understanding of what we think software can do but also creates a paradigm shift on how we should engineer software systems. This project is thus motivated under such a context, aiming to explore cutting-edge methods that underpin the synergy between LLM and Software Engineering (SE). The concrete project topic is rather open but will sit in one of the two themes: LLM for SE or SE for LLM.

In LLM for SE, the project will investigate how LLM can be better tailored and turned into a more specialized variant, hence better solving the software engineering tasks at hand. There is no constraint on the software engineering task to solve, which can include, but is not limited to, software defect prediction, bug report analysis, configuration performance learning/tuning, and software testing. Under the SE for LLM, this project will look at novel ways that can properly evaluate, test, and verify the different properties of LLM-assisted tools, including their robustness, fairness, and security. In essence, for intelligent software engineering in the era of LLM, both themes in the project will work on a creative combination of software engineering knowledge with the characteristics of LLM, and such a unique combination will inevitably lead to both challenges and opportunities, making this research as a fruitful direction for a PhD topic.

The deliverable of the project can be of diverse forms, including methodology, algorithms, or the actual prototype of the tool, depending on the identified nature of the research. You will work in a group of 8 PhD students under close supervision with the chance to work with several industrial collaborators of the group. We offer generous support and a friendly environment. The ideal candidate should have knowledge of AI and/or SE, but it is not essential. Interested applicants are strongly advised to contact Dr. Chen to discuss more details ().


First or Upper Second Class Honours undergraduate degree and/or postgraduate degree with Distinction (or an international equivalent). We also consider applicants from diverse backgrounds that have provided them with equally rich relevant experience and knowledge. Full-time and part-time study modes are available.

We want our PhD student cohorts to reflect our diverse society. UoB is therefore committed to widening the diversity of our PhD student cohorts. UoB studentships are open to all and we particularly welcome applications from under-represented groups, ethnic minorities, individuals with disabilities, and neurodiverse candidates. We also welcome applications for part-time study.