Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming

• URL: http://arxiv.org/abs/2511.18849v1
• Date: Mon, 24 Nov 2025 07:42:07 GMT
• Title: Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming
• Abstract summary: Large Language Models (LLMs) are increasingly integrated into code editors to provide AI-powered code suggestions.<n>Yet many of these suggestions are ignored, resulting in wasted computation, increased latency, and unnecessary interruptions.<n>We introduce a lightweight pre-filtering model that predicts the likelihood of suggestion acceptance before invoking the LLM.
• Score: 0.24160362166617352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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