Measuring programming language popularity based on the number of unique users on GitHub: https://www.benfrederickson.com/ranking-programming-languages-by-github-users/
Whilst this gives the dataset a FOSS bias, it's much cleaner data than e.g. a keyword search of job postings which some other metrics use.
miniblog.
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I don't care what your project's test coverage is, but the fact that you're measuring it is a great sign.
On the challenge of measuring spam prevalence externally, because historical data tends to be cleaner:
Measuring Go program (GoAWK) performance from 1.2 to 1.18: https://benhoyt.com/writings/go-version-performance/
The Go 1.3 runtime led to a 2x speedup in GoAWK, and performance wins have continued since. It's easy to see popular PLs as largely finished, but the wins here show there's still opportunities.