Large language models (LLMs) are rapidly transforming the practice of software engineering. Code correctness has been the primary benchmark for evaluating these models, but real-world software development requires much more. In this talk, I will discuss how we could move beyond correctness to address broader dimensions of LLM-generated code. I will use my recent work on code efficiency, fairness, and diversity as examples, and discuss the challenges and opportunities that lie ahead in advancing LLMs for code.

Bio: Dr. Jie M. Zhang is a lecturer of computer science at King’s College London. Her main research interests are the trustworthiness of software engineering, AI, and LLMs. She has published numerous papers in top-tier venues including ICML, ACL, NeurIPS, ICLR, ICSE, FSE, ASE, ISSTA, TSE, and TOSEM. She is a steering committee member of conferences IEEE ICST and ACM AIware. She is the general chair of AIware 2025, the area chair of ICSE 2026 and ASE 2025, and the program chair of many events such as AIware 2024, Internetware 2024, and ISSTA 2025 Doctoral Symposium. Over the last three years, she has been invited to give over 30 talks at conferences, universities, and IT companies. In recognition of her influence, she was named one of the Top 15 Global Chinese Female Young Scholars in Interdisciplinary AI (2023). Her research has won the FSE 2025 distinguished paper award, the 2022 IEEE Transactions on Software Engineering Best Paper award, and the ICLR 2022 spotlight paper award. She is also the winner of 2025 ACM Sigsoft Early Research Award, one of the most prestigious honours for early-career researchers in the software engineering community.