I'm looking to integrate automated code review into our CI/CD pipeline without requiring additional tools beyond GitHub Actions, which we currently use for our other processes. Right now, we have a basic setup that includes linting, unit tests, and a security scan with Snyk. While that's working okay, it doesn't catch logic errors or issues related to code quality. My senior developers still need to perform manual reviews, which is quite time-consuming. I've explored a few options, but they tend to be costly or need extensive setup, and honestly, we can't afford to manage another complex tool. What solutions are others currently finding effective in production? It would be great if they also integrate well with GitHub Actions and won't slow down our build times, which already take about 8 minutes.
3 Answers
One strategy I recommend is to run checks on commits or pull requests. That way, you can flag or reject any pull requests that don’t meet linting, unit, or security test standards. For the deeper logic issues, manual testing might still be necessary since no tool can understand the full context like a developer can. Overall, the goal should be to have functional code through CI/CD. If your developers can pass all tests and the code still seems off, it might be time to revisit your team's capabilities rather than just relying on CI/CD to solve everything.
It's interesting that your seniors are catching specific logic issues. Are they primarily identifying business logic problems, performance issues, or something else? I'm asking because we're facing similar challenges and I'm curious if any automated tools can handle those situations, or if human oversight will always be necessary. By the way, 8 minutes for builds isn’t ideal, but I’ve seen worse in other projects!
If you really want to cut down on manual reviews, maybe consider loosening your approval rules and allowing direct merges. Using AI for reviews can be helpful, but it should really just supplement human input, not replace it.
Most of the time, they catch things like race conditions, edge cases with nulls, and inefficient queries. These aspects might work fine during testing but could cause real headaches in production.