让AI重构代码更可信，提升开发者使用意愿
Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring
- 在IDE中嵌入可信防护机制，确保AI重构不破坏代码
- 通过用户交互设计引导合理信任，避免盲目依赖
- 结合真实项目数据做大规模测试，持续优化方案
在软件行业中，新增功能常压倒代码改进。大型语言模型（LLMs）为大规模代码重构提供了新路径，但存在引入错误或安全漏洞的风险。本文主张将模型交互封装在IDE中，并通过可信保障机制验证重构行为。同时强调信任建设对推广至关重要。基于人机协作研究框架，本文提出未来工作：1）开发新型LLM防护机制；2）设计能传递恰当信任水平的用户交互方式。通过与产业界合作，利用大规模代码库分析和A/B测试，持续指导研究干预的设计与优化。
原文摘要 · Abstract (English)
In the software industry, the drive to add new features often overshadows the need to improve existing code. Large Language Models (LLMs) offer a new approach to improving codebases at an unprecedented scale through AI-assisted refactoring. However, LLMs come with inherent risks such as braking changes and the introduction of security vulnerabilities. We advocate for encapsulating the interaction with the models in IDEs and validating refactoring attempts using trustworthy safeguards. However, equally important for the uptake of AI refactoring is research on trust development. In this position paper, we position our future work based on established models from research on human factors in automation. We outline action research within CodeScene on development of 1) novel LLM safeguards and 2) user interaction that conveys an appropriate level of trust. The industry collaboration enables large-scale repository analysis and A/B testing to continuously guide the design of our research interventions.
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