# Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study

arXiv: https://arxiv.org/abs/2606.12231 · HTML: https://mlsanity.com/paper/2606.12231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 101 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 30 of 35)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026)
- [AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities](https://mlsanity.com/paper/2508.11126/) (2025)
- [A Survey of Vibe Coding with Large Language Models](https://mlsanity.com/paper/2510.12399/) (2025)
- [Agent READMEs: An Empirical Study of Context Files for Agentic Coding](https://mlsanity.com/paper/2511.12884/) (2025)
- [Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025](https://mlsanity.com/paper/2512.14012/) (2025)
- [Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?](https://mlsanity.com/paper/2602.11988/) (2026)
- [Agentic Refactoring: An Empirical Study of AI Coding Agents](https://mlsanity.com/paper/2511.04824/) (2025)
- [On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents](https://mlsanity.com/paper/2601.20404/) (2026)
- [DeepCode: Open Agentic Coding](https://mlsanity.com/paper/2512.07921/) (2025)
- ["Your AI, My Shell": Demystifying Prompt Injection Attacks on Agentic AI Coding Editors](https://mlsanity.com/paper/2509.22040/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projects](https://mlsanity.com/paper/2511.09268/) (2025)
- [Programming by Chat: A Large-Scale Behavioral Analysis of 11,579 Real-World AI-Assisted IDE Sessions](https://mlsanity.com/paper/2604.00436/) (2026)
- [Software Architecture Meets LLMs: A Systematic Literature Review](https://mlsanity.com/paper/2505.16697/) (2025)
- [Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding](https://mlsanity.com/paper/2509.12491/) (2025)
- [RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository](https://mlsanity.com/paper/2601.13943/) (2026)
- [Harness Engineering for Agentic AI Coding Tools: An Exploratory Study](https://mlsanity.com/paper/2602.14690/) (2026)
- [The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering](https://mlsanity.com/paper/2507.15003/) (2025)
- [The Promptware Kill Chain: How Prompt Injections Gradually Evolved Into a Multistep Malware Delivery Mechanism](https://mlsanity.com/paper/2601.09625/) (2026)
- [Are Coding Agents Generating Over-Mocked Tests? An Empirical Study](https://mlsanity.com/paper/2602.00409/) (2026)
- [Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment](https://mlsanity.com/paper/2509.05755/) (2025)
- [AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans](https://mlsanity.com/paper/2509.21891/) (2025)
- [Your Harness is Not Secure: Benchmarking Real-world Threat of Command Line Interface Agent](https://mlsanity.com/paper/2510.06607/) (2025)
- [Structured Context Engineering for File-Native Agentic Systems: Evaluating Schema Accuracy, Format Effectiveness, and Multi-File Navigation at Scale](https://mlsanity.com/paper/2602.05447/) (2026)
- [Persistent Human Feedback, LLMs, and Static Analyzers for Secure Code Generation and Vulnerability Detection](https://mlsanity.com/paper/2602.05868/) (2026)
- [Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration](https://mlsanity.com/paper/2509.10652/) (2025)
- [Screen Reader Programmers in the Vibe Coding Era: Adaptation, Empowerment, and New Accessibility Landscape](https://mlsanity.com/paper/2506.13270/) (2025)
- [Instruction Adherence in Coding Agent Configuration Files: A Factorial Study of Four File-Structure Variables](https://mlsanity.com/paper/2605.10039/) (2026)

## Built on by (most cited first, 1 of 1)

- [Who Maintains Agent Skills? A Longitudinal Study of Human-Governed, AI-Assisted Skill Maintenance](https://mlsanity.com/paper/2609.05677/) (2026)

## Abstract (arXiv)

The adoption of AI-powered Integrated Development Environments (AI IDEs) has introduced "Rules" as a novel software artifact, allowing developers to persistently inject project-specific constraints and architectural guidelines into the context of Large Language Models (LLMs). Despite their role in aligning AI behavior with developer intent, the taxonomy, evolution, and practical impact of these rules remain largely unexplored. To bridge this gap, we conducted a mixed-methods empirical study on AI IDE rules. By mining 83 open-source projects and extracting 7,310 rules, we established a comprehensive taxonomy comprising 5 primary and 25 secondary categories. We then triangulated these artifacts with survey responses from 99 practitioners. Our analysis identified a contrast between developer priorities and actual configurations: while practitioners rate architectural constraints as highly important, rule files in repositories primarily consist of low-level workflow and code formatting constraints. Furthermore, our analysis of 1,540 rule evolution events revealed that rules are updated frequently. Repository data further indicate that rule evolution is primarily driven by constructive context expansions (29.17%) and enrichments (26.59%). In contrast, surveyed developers reported modifying rules primarily to correct AI errors (77.78%), typically by adding new negative constraints rather than editing existing ones. Finally, an artifact compliance assessment of 160 rule evolution events revealed that updating rules significantly improves the adherence of software artifacts, with the average artifact compliance rate increasing by 22.99% (from 49.14% to 72.13%) following an update. Our study provides empirical insights that can help developers optimize prompting strategies and guide tool builders in designing automated conflict-detection and context-management mechanisms for AI IDEs.