让代码助手不仅给答案，还讲清楚为什么这么写。
Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
- 通过双层交互界面还原AI的思考过程。
- 揭示代码修改背后的上下文影响因素。
- 适合想理解AI决策、提升信任感的开发者。
AI驱动的代码助手广泛用于生成代码补全，显著提升开发效率。然而，这些工具通常仅提供建议而不解释其依据，导致决策过程不透明。这削弱了开发者评估输出、建立准确认知模型以及合理信任系统的能力。为此，我们提出CopilotLens，一种新型交互式框架，将代码补全重构为可解释的透明互动。CopilotLens作为解释层，通过动态双层界面重建AI代理的“思考过程”，旨在揭示高层次代码变更及其受具体代码库上下文的影响。本文阐述了CopilotLens的设计理念与实现逻辑，提供了一个具体的框架，并展望未来通过实证评估深化理解与建立合理信任的预期。
原文摘要 · Abstract (English)
AI-powered code assistants are widely used to generate code completions, significantly boosting developer productivity. However, these tools typically present suggestions without explaining their rationale, leaving their decision-making process inscrutable. This opacity hinders developers' ability to critically evaluate outputs, form accurate mental models, and calibrate trust in the system. To address this, we introduce CopilotLens, a novel interactive framework that reframes code completion from a simple suggestion into a transparent, explainable interaction. CopilotLens operates as an explanation layer that reconstructs the AI agent's "thought process" through a dynamic, two-level interface. The tool aims to surface both high-level code changes and the specific codebase context influences. This paper presents the design and rationale of CopilotLens, offering a concrete framework and articulating expectations on deepening comprehension and calibrated trust, which we plan to evaluate in subsequent work.
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