The CodeBeaver Blog

Let’s face it: code reviews are essential, but they’re not always fun. They can be slow, tedious, and sometimes subjective. And while we all know they’re critical for catching bugs and maintaining code quality, they often feel like a bottleneck in the development process. 

As developers, we’ve all been there: you write a piece of code, it passes all the tests, and you think it’s good to go. Then, weeks later, someone finds a bug in production that could’ve been caught with better test coverage. Or worse, you’re the one stuck fixing it. What if there was a way to make code reviews faster, more reliable, and less reliant on human effort?

I present to you a new buzzword: Automated Test-Driven Code Review.

What Is Automated Test-Driven Code Review?

This idea comes, in part, from reading this paper from Meta. Meta’s engineers started using LLMs to improve their test suites automatically. I want to expand on this idea: at its core, Automated Test-Driven Code Review is a process that combines automated testing with LLM-powered code analysis. Here’s how it works:

1. Your CI/CD pipeline runs the tests—just like it always does.
2. An LLM analyzes the test results, identifies failures, and evaluates the code for potential issues.
3. The LLM provides feedback on your PR, pointing out problems and suggesting improvements.
4. If your test coverage is lacking, the LLM can even generate new tests to fill the gaps.

Automated Test-Driven Code Review helps prevent these headaches by:

• Catching bugs early: By analyzing test results, it flags issues before they make it to production.
• Improving test coverage: It identifies gaps in your tests and can even generate new ones to cover edge cases or untested scenarios.
• Saving time: Instead of waiting for a human reviewer to comb through your code, the LLM does the heavy lifting, leaving your team free to focus on more complex tasks.

How It Works in Practice

To implement Automated Test-Driven Code Review, you’ll need a tool that integrates with your CI/CD pipeline and provides the LLM capabilities to analyze test results and review code. The first tool to implement this workflow is CodeBeaver.

CodeBeaver is able to check your code (either from a PR, from a commit, or reading files from a branch), run the tests in a dedicated instance, find and fix existing flaky tests, tell you if there are bugs in your code that make the test fail, and even write new tests to increase code coverage.

This is an example of a PR review CodeBeaver can generate:

You can check it out here. For this PR, CodeBeaver updated 2 tests, found one bug in my code, and added 3 new tests.

CodeBeaver uses a YAML configuration file (similar to a docker-compose file) to define the testing environment. The simplest codebeaver.yml file looks like this:

from: pytest

It leverages a template that installs and runs an environment to run python tests with pytest.

The configuration file can go as complex as you need, allowing you to define services, environments, and mono-repo workspaces:

workspaces:
  - name: django
    path: backend
    from: pytest
    main_service: localdjango
    environment:
      - DJANGO_SECRET_KEY=test
    services:
      localdjango:
        build:
          context: .
          dockerfile: ./compose/local/django/Dockerfile
        image: codebeaver_local_django_codebeaver
        container_name: codebeaver_local_django_codebeaver
        depends_on:
          - redis
          - postgres
      redis:
        image: redis:6
        ports: ["6379:6379"]
      postgres:
        build:
          context: .
          dockerfile: ./compose/local/postgres/Dockerfile
        ports: ["5432:5432"]
        user: postgres
  - name: react
    path: react
    from: jest

The Bottom Line

As someone who’s spent way too many late nights debugging production issues, I’m all for anything that makes my life easier. Automated Test-Driven Code Review isn’t just a fancy buzzword—it’s a practical solution to a real problem.

If you’re tired of dealing with buggy code, missing test coverage, and endless code reviews, it’s worth exploring this approach. Whether you’re using an open-source solution, building your own, or trying out a service like CodeBeaver, the benefits are clear: better code, fewer bugs, and more time to focus on what really matters—building great software.