Comment by jazzypants
3 hours ago
The point is that LOC is never a good metric for any aspect of determining the quality of code or the coder because it ignores the nuance of reality. It's impossible to generalize because the code can be either deceptively dense or unnecessarily bloated. The only thing that actually matters is whether the business objective is achieved without any unintended side effects.
> The only thing that actually matters is whether the business objective is achieved without any unintended side effects.
Objectives change; timeliness matters. The speed at which you deliver value is incredibly important, which is why it matters to measure your process. Deceptively dense is what I’d call software engineers who can’t accept that the process is actually generalizable to a degree and that lines of code are one of the few tangible things that can be used as a metric. Can you deliver value without lines of code?
> Objectives change; timeliness matters. The speed at which you deliver value is incredibly important, which is why it matters to measure your process.
This assumes that shorter code is faster to write. To quote Blaise Pascal, "I would have written a shorter letter, but I did not have the time."
> Can you deliver value without lines of code?
No, but you can also depreciate value when you stuff a codebase full of bloated, bug-ridden code that no man or machine can hope to understand.
You seem determined to misinterpret. I’m not talking about LOC as a measure of productivity. The ratio of LOC needing review to the capacity of reviewers (using how many LOC can be read/reviewed over the sampling period) is what’s being discussed. Agentic AI/vibe coding has caused that ratio to increase and shows a bottleneck in the SDLC. It’s a proxy metric, get over yourself.
“All models are wrong, some are useful”. What’s not useful is constantly bitching about how there’s no way to measure your work outside of the binary “is it done” every time process efficiency is brought up.
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