Speed is up, but is quality keeping pace?
Track code health, review effectiveness, defects and rework, then trace each back to the human, AI assistant or agent behind the change. As a result, you ship faster without paying for it later.
| Source | PRs | Rework | Churn | Reverts |
|---|---|---|---|---|
| Human | 812 | 9% | 11% | 6 |
| AI assisted | 436 | 13% | 15% | 5 |
| Agent authored | 94 | 19% | 22% | 3 |
AI writes more code than ever. But someone still has to live with it.
Defects show up too late
Churn and rework from AI written code surface weeks after merge, when the context is gone. As a result, every fix costs more.
Review is stretched too thin
Generation scales faster than review, so more changes merge with less scrutiny. Meanwhile, rubber-stamped approvals start to look normal.
No attribution
When quality drops, nobody can tell whether humans, assistants or agents caused it. So teams argue about the cause instead of fixing it.
See how every merge was really reviewed before it shipped.
Merge Quality at a glance
Every merged pull request sorted into thoroughly reviewed, rubber-stamped or unreviewed, week by week, so you see where review discipline is slipping. That way, you can step in before rubber-stamping becomes a habit.
Merge quality overview
Total PRs merged, as well as the share that were thoroughly reviewed, rubber-stamped or unreviewed.
Review quality breakdown
The same split for every week, so trends stand out.
Filter by repo and team
Then narrow down to repo groups, repos, attributes or teams in one click.
Then drill into any metric
When you open a metric like Thoroughly Reviewed PRs, you see the trend against the previous period and how every team compares, week by week. Because every value links to its PRs, you can also act on what you find.
Trend vs previous period
Know whether review depth is improving or slipping before it shows up in production.
Team breakdown
Contributors, mean and trend for every team in one table, so you can compare them side by side.
Drill down to the PRs
Finally, click any value to see the pull requests behind it.
| Team | Contributors | Mean | Trend | vs Previous |
|---|---|---|---|---|
| Data | 118 | 47.4% | +2.8 | |
| Backend | 104 | 43.1% | +1.9 | |
| Frontend | 96 | 49.6% | +4.2 | |
| Platform | 88 | 51.3% | +3.5 |
Quality signals, finally attributed to their source.
Live in your stack instead of another spreadsheet.
Connect your stack
First, link Git (GitHub, GitLab, Bitbucket, Azure DevOps), Jira, CI/CD and your AI coding tools with read-only access.
Waydev maps the work
Then every PR, review, ticket and AI session is tied to a team, an initiative and the tool or agent that produced it.
Act on Signals when they fire
Finally, Waydev Agent flags what changed, suggests Goals, and answers follow up questions in plain language.
Because Waydev only reads engineering metadata through read-only APIs, source code content is never ingested. Visit the Trust Center
What engineering leaders ask before rolling out Waydev
Does Waydev read our source code when it judges quality?
How is quality split by human and agent when both touch a PR?
Can we set quality targets too?
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