How LinearB Calculates AI Attribution
Learn how LinearB identifies AI-assisted development across your codebase. This article explains how AI attribution is calculated using commit signals and AI tool data, how confidence thresholds work, and how results are applied to commits and pull requests.
Overview
LinearB identifies and measures AI-assisted development by combining explicit Git signals with AI tool usage signals that are correlated with coding activity in Git.
This article explains, at a high level, how LinearB determines whether code changes show signs of AI assistance and how that attribution is reflected in AI Analytics and related reporting.
AI Attribution Methods
LinearB uses two primary signal types to identify AI-assisted development:
1. Git-Based Attribution
Some AI tools add metadata to commits when developers create code using the tool.
- How it works: The commit includes a
Co-authored-bysignature or similar Git metadata added by the AI tool. - What this means: When this metadata is present, LinearB can identify the commit as AI-assisted based on the Git signal.
- Limitations: Not all tools or developer workflows add this metadata automatically.
2. AI Tool Usage Signals
When Git metadata is not available, LinearB uses heuristic signals from connected AI tools.
- How it works: LinearB correlates AI tool usage API data with coding activity in Git.
- Data sources: Supported AI tools such as Claude, GitHub Copilot, Cursor, and Amazon Q.
- What this means: LinearB can identify patterns that indicate AI-assisted development even when explicit Git metadata is not present.
- Timing: AI tool usage data may be processed after the coding activity occurs.
Commit-Level Attribution
LinearB evaluates commits using the available AI attribution signals.
- Git-based attribution is used when explicit AI-related Git metadata is present.
- Heuristic attribution is used when AI tool usage signals correlate with Git activity.
The available signals determine whether commit activity is classified as AI-assisted in LinearB reporting.
Pull Request Attribution
LinearB reflects AI attribution at the pull request level because a pull request represents a complete unit of work.
When LinearB detects AI-assisted activity associated with a pull request, that pull request may be classified as AI-assisted in AI Analytics and related reporting.
Data Requirements
- Git data must be properly synced in LinearB.
- AI tool integrations must be connected for AI tool usage signals to be available.
- AI tool users must be mapped to LinearB users so activity can be associated with the correct contributors.
Limitations
- Not all AI tools add Git metadata to commits.
- AI tool usage signals depend on the data available from each connected AI provider.
- AI usage data may be delayed depending on the provider and integration path.
- Code copied manually from an AI chat interface may not be detected unless it is represented in a supported signal.
- Different AI tools provide different levels of signal coverage.
Where AI Attribution Appears
- AI Analytics
- AI Insights
- AI Adoption
- AI-related views and filters across LinearB reporting, where supported
FAQ
Is AI attribution always exact?
Some signals, such as explicit Git metadata, provide direct evidence of AI usage. Other signals are heuristic and depend on correlation between AI tool usage and Git activity.
Why is AI usage missing for some developers?
This may occur if AI tools are not connected, usage data is delayed, or AI tool users are not mapped to LinearB users.
Does LinearB detect every AI interaction?
No. LinearB relies on supported Git and AI tool signals. Some workflows, such as manually copying code from an AI chat interface, may not be detected.
Why can AI attribution differ between tools?
Different AI tools expose different types of usage data and metadata, so the available attribution signals may vary by provider.
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