Measuring the sloppiness of code

In one line

Exploring how to measure code sloppiness, why correct code can still erode a codebase, and why human intuition and taste still matter.

Opening excerpt

LLMs have become almost perfect at generating code, but that isn’t the end of the story. Just because the code is formally correct doesn’t mean that it is not introducing unnecessary abstractions, creating duplicates, or just making bad decisions overall. This is not a groundbreaking observation, most people who have vibe-coded a project, have realized that each additional feature can sometimes lead to an explosion of lines of code (LOC).

This results in a loss of human agency, because in projects that are adding millions of LOC per month, it is hard for humans to keep up. 1 Some people might say that that is not an issue at all, because they trust their agents to deal with it. I have bad news for you, agents can’t really deal with the slop either.

Coming from a physics background, I always had an experimental/quantitative approach to solving problems. When I started at Earendil, with the task of figuring out how to measure code sloppiness, my natural instinct was to first take a deep dive into the literature and then check what other companies were doing.

(Excerpted from the original; full article via the source link below.)

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