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

arXiv: https://arxiv.org/abs/2511.18849 · HTML: https://mlsanity.com/paper/2511.18849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT](https://mlsanity.com/paper/2304.10778/) (2023)
- [The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers](https://mlsanity.com/paper/2404.02806/) (2024)
- [When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming](https://mlsanity.com/paper/2306.04930/) (2023)
- [Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise](https://mlsanity.com/paper/2412.06603/) (2024)
- [Prompt-based Code Completion via Multi-Retrieval Augmented Generation](https://mlsanity.com/paper/2405.07530/) (2024)
- [The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot](https://mlsanity.com/paper/2410.02091/) (2024)
- [Overwatch: Learning Patterns in Code Edit Sequences](https://mlsanity.com/paper/2207.12456/) (2022)
- [Experience with GitHub Copilot for Developer Productivity at Zoominfo](https://mlsanity.com/paper/2501.13282/) (2025)
- [A Transformer-Based Approach for Smart Invocation of Automatic Code Completion](https://mlsanity.com/paper/2405.14753/) (2024)
- [Understanding User Mental Models in AI-Driven Code Completion Tools: Insights from an Elicitation Study](https://mlsanity.com/paper/2502.02194/) (2025)
- [Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP)](https://mlsanity.com/paper/2506.11019/) (2025)
- [NES: An Instruction-Free, Low-Latency Next Edit Suggestion Framework Powered by Learned Historical Editing Trajectories](https://mlsanity.com/paper/2508.02473/) (2025)
- [Predicting Developers' IDE Commands with Machine Learning](https://mlsanity.com/paper/2010.05036/) (2020)

## Built on by (most cited first, 2 of 2)

- [Loc2Repair: A Framework for Evaluating the Impact of File-Level Issue Localization in Repo-Level LLM Repair](https://mlsanity.com/paper/2606.30963/) (2026)
- [Comparing Developer and LLM Biases in Code Evaluation](https://mlsanity.com/paper/2603.24586/) (2026)

## Abstract (arXiv)

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.