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Comment by salmonellaeater | Hacker News Reader

Of note is that these are all associations. It's possible the associations are all due to selection effects (or even that the "better" language are actually worse but their effects are reversed by selecting for programmers who are better).

In the early days of Python, some employers used it as a way to filter for candidates who were ahead of the curve. I believe Paul Graham mentioned Lisp being good for attracting better programmers, although I can't find the reference anymore [1]. If there's any value in this kind of filter, the effects will show up in the programmers' output.

1. I'm having a hard time finding references for these assertions; the best I've found is some Reddit posts and Joel Spolsky's post on finding great developers: https://www.joelonsoftware.com/2006/09/06/finding-great-deve...

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The paper goes further though. It states: Not only is it not possible to establish a causal link between programming language and code quality based on data at hand, but even their correlation proves questionable.

I find that a surprising result! This might also call into question the theory that some languages attract better developers.

In an infamous rant, Linus Torvalds also hints at using a language as a filter for programmers[1]:

>Quite frankly, even if the choice of C were to do nothing but keep the C++ programmers out, that in itself would be a huge reason to use C

[1] http://harmful.cat-v.org/software/c++/linus

Considering the astonishing density of defects in Linux we can treat its author’s advice on software development as anti-advice.

What is the density of defects in Linux? Any published comparisons?

According to Coverity, the "defect density" of the Linux kernel is around 0.5 errors per 1000 lines of code, while the average of the analyzed open source projects is around 0.7. Best among the analyzed projects is Python (the language/stdlib I guess) with an error rate of 0.08.

I don’t know about relative quality but Syzkaller finds a new kernel bug every minute or two and Coverity says there are thousands of unfixed defects that their static analysis can detect.

and yet here we are in 2019 where linus happily contributes to a C++ codebase (https://github.com/Subsurface-divelog/subsurface)

He's a good programmer though, not the kind he wanted to keep out.

Indeed, and in addition to selection bias towards better programmers, there is another selection bias implicit in this.

A shop that makes the decision to write their stack in Haskell do it with an intention to focus on correctness. I reckon that such companies would consequently invest in other practices like more tests, higher code coverage and more intensive code reviews resulting in code with fewer errors.

This is in stark contrast to a startup that may have hacked together a POC in Python, and once it worked, with a little bit more spit and polish put it into production.

> In the early days of Python, some employers used it as a way to filter for candidates who were ahead of the curve. I believe Paul Graham mentioned Lisp being good for attracting better programmers

Possibly "The Python Paradox" [1] and/or "Beating the Averages" [2]. (The first one, from 2004, is a bit of an odd read nowadays, since it no longer applies to Python.)

[1] http://www.paulgraham.com/pypar.html [2] http://www.paulgraham.com/avg.html

> It's possible the associations are all due to selection effects

FWIW, the original authors (Ray et al) said as much.

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