Hey everyone! I've noticed a lot of teams, including some of my friends and ex-colleagues, are heavily using AI tools for code generation. While it can save time and feel almost magical, it raises concerns about the safety, scalability, and maintainability of the code being produced. As someone who freelanced in the past, I've seen codebases that worked great initially but turned into maintenance nightmares with security bugs and poor structure over time because of a lack of review on AI-generated code.
I'm curious about your experiences! Do you have processes in place for checking AI-generated code when it comes to security, scalability, maintainability, and modularity? If you do, what seems to be working for you? Is it all manual reviews, automated tools, CI/CD scans, or something else? And for those who are coding quickly with AI, what's your strategy to ensure the code remains scalable and secure? I'm all ears for your stories, frustrations, or wishlist ideas!
11 Answers
I mostly use AI for quick prototypes and simple scripts, but I wouldn't trust it for production-level coding just yet. There’s no replacement for a skilled developer.
But the reality is that many won’t even bother until there's a major incident. We're likely to see the implications of all this AI noise down the road when the debts pile up.
Tools like SonarQube are fantastic for managing code quality and can be integrated into our builds. This helps in catching potential issues before they escalate.
We approach this like we do with less experienced devs: using peer reviews and thorough testing (unit, integration, etc.). We want to keep the quality consistent, regardless of the source.
The same quality checks apply, but I find it hard to keep up with the volume of code being churned out by AI. Managing a massive number of PRs can be overwhelming when AI tools keep pumping out hundreds of lines daily.
We must have some kind of tool for checking code complexity and vulnerabilities, like SAST tools. Our implementation is all about regular scans and making sure we keep our code quality in check.
Honestly, I treat AI-generated code just like any other code. We run it through our usual testing protocol: automated unit tests, deploy in dev, integration tests, and we do manual PR reviews. It all gets the same scrutiny regardless of how it was written.
That's a solid approach! I think as long as the testing remains thorough, it shouldn't matter too much whether it’s AI-generated or not.
Exactly, but sometimes AI-generated changes can clutter PRs with excessive code and comments, which makes reviews a bit more tedious.
I'm also intrigued about how to manage security concerns. In many companies, security seems like an afterthought unless someone brings it up. It's something we definitely need to address more.
To me, it doesn't matter if the code is human or AI-generated. If the tests fail or the code is subpar, it gets sent back for revision. Plus, I believe AI still has a long way to go before it can replace solid engineering.
Our practices don't change with the introduction of AI; we maintain pipelines, testing, security checks, and reviews as we always have. Low-quality AI code gets flagged just like human-written code would.
We have strict standards and human oversight in our reviews. If an engineer continuously tries to push low-quality AI code, they eventually struggle to get their PRs approved. We've set the bar high, which helps maintain quality.
Sounds intense! But I guess that's necessary to keep the codebase clean.
Right! It's all about maintaining those standards and ensuring any code, AI or otherwise, passes muster.