软件工程中人工智能的挑战与发展路径

Challenges and Paths Towards AI for Software Engineering

March 28, 2025

作者: Alex Gu, Naman Jain, Wen-Ding Li, Manish Shetty, Yijia Shao, Ziyang Li, Diyi Yang, Kevin Ellis, Koushik Sen, Armando Solar-Lezama

cs.AI

摘要

近年来，AI在软件工程领域取得了显著进展，成为生成式AI中的一大亮点。然而，在自动化软件工程充分发挥其潜力之前，仍有许多挑战亟待解决。我们有望实现高度自动化，使人类能够专注于构建内容的关键决策以及如何平衡复杂的权衡，而大部分常规开发工作则由自动化完成。要达到这一自动化水平，需要学术界和工业界投入大量的研究和工程努力。本文旨在从三个方面探讨这一进展。首先，我们提供了一个关于AI在软件工程中具体任务的结构化分类，强调除了代码生成和补全之外，软件工程中还有许多其他任务。其次，我们概述了当前方法面临的几个关键瓶颈。最后，我们列出了一份富有见解的研究方向清单，旨在推动这些瓶颈的突破，期望能激发这一快速成熟领域的未来研究。

English

AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.