DeepScan VS coderot

coderot's answer:

Choose coderot if your team lets AI agents write a growing share of the code and you want to know whether it's paying off or quietly making the codebase harder to change.

Linters and quality gates check each change as it's merged. coderot shows the long-term result: whether complexity and duplication grew after agents took over, in which folders, and which agents, PR sizes and review habits drove it. You see whether your guardrails actually work.

It's quick to try and low-risk. You add one workflow file and get a year of history on the first run, with no server to host. Your source code never leaves your CI, so it can't leak from us and you don't need a security review to grant repo access.

The AI numbers are honest. coderot never guesses who wrote the code, and every number shows how much of the code it could attribute.

coderot's answer:

coderot shows whether your codebase got harder to maintain after AI agents took over, and what drove it. It charts complexity, duplication and hotspots per folder, month by month, marks the month most merged PRs started coming from agents, and names the PRs behind each jump. It also links that to which agent wrote them, how big they were and how much review they got. Other code-quality tools measure the code but don't know what wrote it or how it was merged.

Your code never leaves your CI. Analysis runs as a GitHub Action and uploads only numbers, file paths and function names, never source, and coderot gets no read access to your repo.

It never guesses who wrote the code. AI attribution comes only from what the tools themselves record: co-author trailers, bot authors and Git AI notes. Every AI number shows how much of the code it could attribute.

It turns the numbers into concrete steps, such as a PR size limit, a second reviewer for one agent's PRs, or lines to add to AGENTS.md. You can mark the day you try one and watch what changes after it.

coderot's answer:

Engineering leads: CTOs, heads of engineering, engineering managers and tech leads at software teams that let AI coding agents write a large share of their code. That means tools like Claude Code, Cursor, GitHub Copilot and autonomous coding agents. They're the ones being asked "is AI making our codebase worse?" and they need an answer with numbers, not impressions.

Staff and senior engineers who own a codebase's health also use it. It tells them which folders to look at first and what to change in the team's process or in the agents' instructions.

Today it fits teams that host code on GitHub, run GitHub Actions, and write C# or TypeScript/JavaScript.

coderot's answer:

TypeScript throughout, plus C# for the C# analyzer.

• Analysis (runs in the customer's CI): a Node.js CLI shipped as a GitHub Action. It parses TypeScript/JavaScript with oxc-parser, parses C# with Roslyn (the .NET compiler platform), and finds duplicated code with jscpd.
• API: Hono on Node.js, PostgreSQL, Zod for validation.
• Dashboard: React, built with Vite.
• Website and docs: Astro.
• Hosting: Cloudflare (Workers, Containers, R2 storage), Neon Postgres.
• CI/CD: GitHub Actions.

coderot's answer:

In 2026, AI agents went from helping with code to writing most of it. In microsoft/vscode, commits with an AI signal went from almost none in early 2025 to about 80% by October 2026, and other big projects followed the same curve. At the same time, each line gets less human review than before.

Every engineering lead we talked to asked the same question: is this making our codebase harder to maintain? The existing tools couldn't answer it. Linters and quality gates check each change as it's merged, and treat agent code like any other. Tools that split AI from human code measure productivity, not maintainability. And once agents write nearly everything, comparing AI with human code stops working: the human side is empty.

So we built coderot to answer the question directly. It measures complexity, duplication and hotspots over a year of history, marks when agents took over, and shows what drove the change. Your code stays in your CI, because a tool that measures your codebase shouldn't need a copy of it.