Pragmatic Automated Code Transformation Leveraging Large Language Models

With recent advancements in Large Language Models (LLMs) designed for coding, Artificial Intelligence for Software Engineering (AI4SE) remains pivotal in enhancing both developer productivity and software quality. However, to excel at more specialized SE tasks like code transformation, companies still need to optimize LLMs to their specific context to avoid LLMs’ tendency to “hallucinate”, i.e., make up certain predictions.

The goal of this partnership is to develop advanced LLM-based methods that enable the translation of code across various software frameworks, focusing on detailed method-level and extensive project-aware file-level transformations. Such code transformation enhances the adaptability of complex software systems to changes in both the functional requirements and technological landscape (e.g., moving away from a legacy framework), ensuring that these systems remain efficient, scalable, and up-to-date with the latest industry standards and practices.

Faculty Supervisor:

Yuan Tian;Bram Adams

Student:

Partner:

Ross Video Limited

Discipline:

Computer science

Sector:

Manufacturing

University:

Queen's University

Program: