---
title: "AI pull request overview"
canonical_url: "https://docs.getdx.com/reports/ai-code-overview/"
md_url: "https://docs.getdx.com/reports/ai-code-overview.md"
last_updated: "2026-05-22"
---

# AI pull request overview
The **AI pull request overview** report shows your existing PR metrics—cycle time, size, revert rate, and more—broken down by the percentage of AI-generated code in each PR. Use it to understand how AI-authored code moves through your development process differently than human-written code.

> This report is powered by [AI Code Insights](https://docs.getdx.com/ai-code-insights/overview/). AI Code Insights must be deployed to developer machines before data will appear in this report.

![AI pull request overview report in DX](https://docs.getdx.com/assets/images/reports/ai-code-overview.png)

## When to use AI pull request overview

This report helps teams:

- **Compare SDLC metrics across AI code levels** — See whether PRs with high AI code percentages move faster, get more review comments, or revert more often than PRs with little or no AI code.
- **Spot quality and velocity signals** — Identify patterns like longer cycle times or higher revert rates for AI-heavy PRs that may point to areas where AI tools need better guidance or oversight.
- **Inform AI tool strategy with data** — Use concrete metrics to decide where to invest in AI tooling, where to adjust review practices, and how to set expectations for AI-assisted development.

## What the report shows

The report is a dashboard of PR metrics, each displayed as a time series chart broken down by AI code percentage band. Each chart shows how that metric differs across levels of AI code contribution.

| Metric                 | What it measures                                                     |
| ---------------------- | -------------------------------------------------------------------- |
| PRs opened             | Number of PRs opened per period                                      |
| PRs merged             | Number of PRs merged per period                                      |
| PR cycle time          | Time from PR open to merge                                           |
| PR size                | Lines of code changed per PR                                         |
| Review comment density | Number of review comments per PR                                     |
| Review turnaround      | Time from review request to first review                             |
| PR revert rate         | Percentage of PRs that were reverted                                 |
| Allocation             | Percentage of PRs categorized as bug fixes, features, or maintenance |

Each metric can be drilled down to the individual PR level.

## How AI code percentage is calculated

Each PR is assigned an AI code percentage based on the code changes in its commits:

**AI additions ÷ (AI additions + human additions) × 100**

Where:

- **AI additions** is the total number of lines added that were classified as AI-authored across all commits in the PR
- **Human additions** is the total number of lines added that were classified as human-authored across all commits in the PR

This percentage is recalculated periodically as new commits are pushed. PRs with no additions (e.g., delete-only PRs) have no AI code percentage and appear in the "Unknown" band.

## Understanding AI code percentage bands

Each PR is grouped into one of five bands based on its AI code percentage. Charts in the report show a separate series for each band.

| Band      | Criteria           | Description                                                           |
| --------- | ------------------ | --------------------------------------------------------------------- |
| 0% AI     | `pct_ai = 0`       | Entirely human-written — no AI-authored lines detected                |
| <33% AI   | `0 < pct_ai < 33`  | Mostly human-written with some AI assistance                          |
| 33–66% AI | `33 ≤ pct_ai ≤ 66` | Roughly equal mix of human and AI-authored code                       |
| >66% AI   | `pct_ai > 66`      | Predominantly AI-authored code                                        |
| Unknown   | `pct_ai` is null   | AI code percentage could not be determined (e.g., no commit data yet) |

Use the **AI code %** filter to focus the report on specific bands—for example, comparing only "0% AI" against ">66% AI" to see the starkest contrasts.
---

## Sitemap

[Overview of all docs pages](/llms.txt)