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I gasped when I saw this:
A bit of discussion indicated that the trigger for the CPU spikes both times was our CEO logging in. We re-deployed to get a clean start, permanently banned him from the service, and moved on.
This is like finding a live grenade under your bed and putting it under the rug.
They found a way to reproduce a system killing bug, and instead of taking the time to understand it, they threw away their test case.
They contained the impact. Root causing or “understanding” should come after impact mitigation. If needed find a safe way to reproduce the bug without customer impact.
We reverted the refactoring, deployed, un-banned the CEO, and set about analysis.
Yeah me too but if you keep reading they didn't actually "move on" in the way that it sounds.
Well done. More and more companies are deploying LLM-written code in production environments. Might as well be honest about the results so we can learn what does and doesn't work.
Why are we using tools that can't parse the comment and code via syntax for refactoring?
The first problem is they're letting AI touch their code.
The second problem is they're relying on a human to pick up changes in moved code while using git's built-in diff tools. There's a whole bunch of studies that show how git's diff algorithms are terrible, and how swapping to newer diff algos improves things considerably.
TL;DR on the studies:
There's also a bunch of alternative diff algos you can use, but the best ones are paid, and the free ones have fewer features. See: