Robust Large Language Models for Intelligent Software Development

Large Language Models (LLMs) are revolutionising software development by automating code generation and optimisation. However, applying LLMs to software development faces one glaring problem: correctness. Asking LLMs to generate the correct code remains a matter of luck. This project aims to make LLMs reliable for software engineering, enabling them to produce accurate and correct code. <br /> <br /> This project will develop techniques to help software engineers complete previously costly and challenging tasks in real-life settings. If successful, this project will lead to fundamental breakthroughs in ML-based code reasoning.<br />

<p style="margin-bottom:11px">Large language models (LLMs) hold immense potential in supporting software engineering tasks like code translation and optimisation, many of which currently require extensive human involvement and are expensive. Automating these tasks can thus offer substantial cost savings. However, applying LLMs to code generation faces one glaring problem: correctness. Asking LLMs to produce correct code remains a matter of luck - they are often wrong than right in many code-related tasks.</p> <p>Our vision is to make LLMs practical and reliable for code generation. To this end, we will develop new learning algorithms and machine learning (ML) model architectures to extract information from structured data, such as program data and dependence graphs. This will enable ML to take advantage of the structured syntax and semantics of programming languages to reason about data flows and dependencies essential for code generation. We will find ways to scale LLMs and formal methods so that they can handle large and complex programs in real-life settings.</p> <p>If successful, this project will lead to fundamental breakthroughs in ML-based code reasoning. Working with our industry partners (TurinTech AI), we will demonstrate how our techniques can assist in code generation and optimisation tasks in real-world industry settings, helping software engineers complete previously costly and challenging software engineering tasks.</p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>EPSRC DTP Engineering & Physical Sciences</strong></em> and in the research information section that the research degree you wish to be considered for is <em><strong>Robust Large Language Models for Intelligent Software Development </strong></em>as well as <a href="https://eps.leeds.ac.uk/computing/staff/6452/professor-zheng-wang">Professor Zheng Wang</a> as your proposed supervisor. <em><strong>Please state clearly in the Finance Section that the funding source you are applying for is EPSRC Doctoral Landscape Award CASE Competition Award 2025/26.</strong></em></p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p>Applications will be considered after the closing date.  Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of Tuesday 8 April 2025:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p>A highly competitive EPSRC Doctoral Landscape CASE Competition Award in collaboration with TurinTech AI, providing full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £20,780 per year and an additional top-up of £4,000 per year for 3.5 years. Training and support will also be provided. As part of this opportunity, the Postgraduate Researcher will also undertake a three-month paid internship at TurinTech AI's London office.</p> <p>This opportunity is open to all applicants.  All candidates will be placed into the EPSRC Doctoral Landscape CASE Competition Award Competition and selection is based on academic merit.</p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about this project, please contact Professor Zheng Wang by email to <a href="mailto:Z.Wang5@leeds.ac.uk">Z.Wang5@leeds.ac.uk</a></p> <p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p>