用开发者操作行为预判代码建议是否被采纳，提升效率
Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming
- 基于打字速度等实时行为数据预筛选建议
- 接受率从18.4%升至34.2%，减少35%低效调用
- 无需读取代码，保护隐私且适合生产环境
大型语言模型（LLMs）正被集成到代码编辑器中提供智能代码建议。然而许多建议未被采纳，造成计算浪费、延迟增加和干扰。本文提出一种轻量级预过滤模型，在调用LLM前仅使用实时开发者行为数据（如打字速度、文件导航、编辑活动）预测建议被接受的可能性。在生产级Visual Studio Code插件中部署四个月的自然使用测试显示，该方法使建议接受率从18.4%提升至34.2%，同时抑制了35%的低价值LLM调用。结果表明，仅依靠行为信号即可显著改善用户体验与系统效率，凸显时序感知、隐私保护型自适应机制的价值。该过滤器仅依赖调用前的编辑器行为数据，从不检查代码或提示内容。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly integrated into code editors to provide AI-powered code suggestions. Yet many of these suggestions are ignored, resulting in wasted computation, increased latency, and unnecessary interruptions. We introduce a lightweight pre-filtering model that predicts the likelihood of suggestion acceptance before invoking the LLM, using only real-time developer telemetry such as typing speed, file navigation, and editing activity. Deployed in a production-grade Visual Studio Code plugin over four months of naturalistic use, our approach nearly doubled acceptance rates (18.4% -> 34.2%) while suppressing 35% of low-value LLM calls. These findings demonstrate that behavioral signals alone can meaningfully improve both user experience and system efficiency in LLM-assisted programming, highlighting the value of timing-aware, privacy-preserving adaptation mechanisms. The filter operates solely on pre-invocation editor telemetry and never inspects code or prompts.
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