Xiongfei Wu

Large Language Models (LLMs) have already reshaped many aspects of traditional software engineering, from code generation and debugging to automated testing and maintenance. As an important research sub-domain in the software engineering area, quantum software engineering (QSE) is poised to undergo a similar transformation. As QSE moves toward broader adoption, including the generation of quantum code, program testing, and automated program repair, LLMs will likely play a key supporting role in its development. However, despite encouraging early attempts, the community still lacks a roadmap for using LLMs in QSE. Without such a roadmap, opportunities may be missed, challenges may remain unrevealed, and risks may be overlooked. In this article, we present our vision for how LLMs can advance QSE, focusing on four core tasks: program modeling and analysis, program generation, testing, and repair. We outline the opportunities and unique challenges, identify the risks inherent in this integration, and propose a research agenda to guide the community toward realizing the full potential of LLM-powered QSE.

Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous driving systems (ADSs) in perception, reasoning, decision-making, and interaction. However, ADSs are safety-critical cyber-physical systems, and integrating FMs into them raises substantial software engineering challenges in data curation, system design, deployment, evaluation, and assurance. To clarify this rapidly evolving landscape, we present an initial roadmap, grounded in a structured literature review, for integrating FMs into autonomous driving across three dimensions: FM infrastructure, in-vehicle integration, and practical deployment. For each dimension, we summarize the state of the art, identify key challenges, and highlight open research opportunities. Based on this analysis, we outline research directions for building reliable, safe, and trustworthy FM-enabled ADSs.