The conclusion is not surprising really, because fundamentally how do you even quantify sloppiness, a famously broad and subjective characterization?

I worked for years in Dev Productivity with engineers who had spent their entire careers in that field, and code quality was always the biggest "unquantifiable". Any of the metrics in the literature (cyclomatic complexity, erosion, etc.) quickly became very noisy at scale. Conversely, for any given metric you would find countless bits of code that do NOT exceed any metric thresholds but were clearly low quality.

People have experimented with many things over many years at Big Tech scale, which produces prodigious volumes of code daily. The conventional wisdom was "Don't bother trying to measure code quality."

An interesting observation from an ex-colleague is that probably the best measure of code quality is its comprehensibility, or "understandability". Maintainability, stability and adaptability are natural outcomes of that. But understanding lies entirely in the mind of the beholder! Which is why it's such a subjective metric, not amenable to simple mechanistic measures.

But now, we probably do have a technology that demonstrates some analog of comprehension: LLMs!

Specifically: tokens. Anecdotally and empirically (based on industry reports like DORA and DX etc.) AI coding works much better with "good codebases" (more specifically, strong engineering discipline) than otherwise. I wonder if that can be parlayed into a quantifiable metric like "tokens to grok / LoC" somehow.

So, if to fix something we need to first measure it, and if AI can measure slop, the way to fix slop from AI may be... more AI!