为生成式AI如何改变软件工程提供系统性研究路线图。
A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI
- 基于设计科学方法，通过多轮研讨与反馈构建路线图。
- 提炼出生成式AI在软件工程中的四种增强形式及其挑战。
- 适合关注AI赋能软件开发的科研人员与从业者参考。
生成式AI（GenAI）正迅速改变软件工程（SE）实践，影响开发、运维和演化过程。本文采用设计科学研究方法，构建了生成式AI增强软件工程的研究路线图。该过程包含三个循环，整合了FSE 2025年“软件工程2030”研讨会的协作讨论、快速文献综述以及外部同行反馈。利用麦克卢汉四极法作为概念工具，系统捕捉生成式AI对软件工程流程与产品的影响。路线图识别出四种基础的生成式AI增强形式，并系统刻画其相关研究挑战与机遇。最终凝练出未来研究方向。通过多轮验证与独立团队交叉确认，研究提供了透明、可复现的分析基础，有助于理解生成式AI对软件工程方法、工具与流程的影响，并为该快速演进领域的未来研究提供框架。
原文摘要 · Abstract (English)
Generative AI (GenAI) is rapidly transforming software engineering (SE) practices, influencing how SE processes are executed, as well as how software systems are developed, operated, and evolved. This paper applies design science research to build a roadmap for GenAI-augmented SE. The process consists of three cycles that incrementally integrate multiple sources of evidence, including collaborative discussions from the FSE 2025 "Software Engineering 2030" workshop, rapid literature reviews, and external feedback sessions involving peers. McLuhan's tetrads were used as a conceptual instrument to systematically capture the transforming effects of GenAI on SE processes and software products. The resulting roadmap identifies four fundamental forms of GenAI augmentation in SE and systematically characterizes their related research challenges and opportunities. These insights are then consolidated into a set of future research directions. By grounding the roadmap in a rigorous multi-cycle process and cross-validating it among independent author teams and peers, the study provides a transparent and reproducible foundation for analyzing how GenAI affects SE processes, methods and tools, and for framing future research within this rapidly evolving area.
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