Copilot Metrics – Valven

Overview

In today’s fast-evolving software development landscape, AI-assisted development has become increasingly common. Tools like GitHub Copilot are widely adopted to accelerate coding, reduce repetitive tasks, and support developer productivity. However, while adoption is high, measuring the actual impact of AI tools remains a challenge for most teams.

Understanding how Copilot is used, by whom, and how it affects your development process is crucial for making informed decisions about your AI strategy. That’s where the Copilot Metrics feature comes in.

This feature analyzes GitHub Copilot usage data across your teams and presents key metrics to evaluate both AI adoption and its impact on engineering workflows.

What Does Copilot Metrics Track?

The following reports are available to help you understand how GitHub Copilot is used within your organization:

Adoption Summary

Number of Active Users
Tracks how many developers in your organization are using GitHub Copilot.
AI Suggestion Frequency
Shows how often users are receiving Copilot suggestions during development sessions.

Usage Effectiveness

Suggestion Acceptance Rate
Measures how frequently Copilot suggestions are accepted and used in code. This is broken down by:
- Programming Language (e.g., JavaScript, Python, Java)
- Code Editor (e.g., VS Code, JetBrains)

Understanding where Copilot is most effective helps you identify ideal use cases and areas for developer enablement.

PR-Level Usage

Pull Requests Summaries Created with Copilot
This metric helps measure how often users are actively leveraging Copilot to create contextual summaries for PRs, providing insight into manual usage of AI-generated content during the code review process.

This helps you understand how actively Copilot is being used in code review workflows, not just during code writing. It indicates where Copilot is contributing suggestions or analysis on proposed changes before they are merged.

Impact Analysis

Beyond basic usage metrics, the Copilot Metrics dashboard helps assess how Copilot affects development processes. For example:

Are Copilot users shipping code faster?
Are Copilot-influenced PRs smaller, more focused, or reviewed faster?
Are certain teams benefiting more from AI assistance than others?

These insights can help you guide AI strategy decisions, justify investment, and track improvements over time.

How to Use the Feature

If you haven't done yet, you need to integrate your Github account with Valven.
Go to the Insights on left bar, and then click on Copilot Metrics.
Select a date range or filter by team.
Explore charts showing usage, adoption, and impact.

Data Availability & Limitations

Upon initial integration, data collection may take some time to appear.
Due to GitHub's data access limitations, Copilot metrics are only available for:
- Teams with 5 or more developers
- Activity from the past 30 days
Once integrated, Valven stores historical Copilot usage data to provide long-term trend analysis and reporting beyond GitHub's default retention.

Why It Matters

AI adoption is no longer a question of if, but how effectively. By tracking how Copilot is used in your organization, you gain:

Transparency into real-world AI usage
Data-driven insight into developer workflows
The ability to shape training, onboarding, or guidelines for better AI leverage

Prerequisites

In order to be able to use this feature, these permissions must be provided:

manage_billing:copilot
read:org
read:enterprise