arXiv:2503.06195cs.SEcs.AI2025-03中稿 · Empirical Software…综述被引 17

研究开发环境中的AI人机体验,揭示提升效率与潜在风险

Human-AI Experience in Integrated Development Environments: A Systematic Literature Review

  • 系统梳理90篇文献,归纳AI助写对开发影响、界面设计与代码质量
  • AI提效但增加验证负担,过度依赖可能引发错误与安全风险
  • 适合关注AI编程工具设计与评估的研究者和开发者

人工智能(AI)融入集成开发环境(IDE)正在重塑软件开发方式,催生出人机交互新范式——在IDE中的人机体验(in-IDE HAX)。尽管应用迅速普及,相关研究仍分散零散,亟需系统性综述。本研究通过对90项文献的系统性回顾,总结现有成果并提出未来方向。将关键发现归为三方面:影响、设计与质量。影响研究显示,AI助写提升开发效率,但伴随验证开销增大与过度依赖问题;设计研究指出,优秀界面应呈现上下文信息、提供建议解释与透明度,并支持用户控制;质量研究揭示了正确性、可维护性及安全性方面的风险。未来研究重点应包括生产力评估、辅助设计优化、以及对生成代码的审计机制。建议开展更大规模、更长期的评估,强化审计与验证资源,覆盖全软件生命周期,并实现用户可控的自适应辅助。

原文摘要 · Abstract (English)

The integration of Artificial Intelligence (AI) into Integrated Development Environments (IDEs) is reshaping software development, fundamentally altering how developers interact with their tools. This shift marks the emergence of Human-AI Experience in Integrated Development Environment (in-IDE HAX), a field that explores the evolving dynamics of Human-Computer Interaction in AI-assisted coding environments. Despite rapid adoption, research on in-IDE HAX remains fragmented, which highlights the need for a unified overview of current practices, challenges, and opportunities. To provide a structured overview of existing research, we conduct a systematic literature review of 90 studies, summarizing current findings and outlining areas for further investigation. We organize key insights from reviewed studies into three aspects: Impact, Design, and Quality of AI-based systems inside IDEs. Impact findings show that AI-assisted coding enhances developer productivity but also introduces challenges, such as verification overhead and over-reliance. Design studies show that effective interfaces surface context, provide explanations and transparency of suggestion, and support user control. Quality studies document risks in correctness, maintainability, and security. For future research, priorities include productivity studies, design of assistance, and audit of AI-generated code. The agenda calls for larger and longer evaluations, stronger audit and verification assets, broader coverage across the software life cycle, and adaptive assistance under user control.

人机交互AI编程开发工具系统综述

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