rjf> The chance that Machine Learning AI programs will find 
rjf> important algorithmic efficiency fixes in algebraic 
rjf> mathematical implementations seems pretty slim.

Improving code is something other than improving algorithmic
efficiency of high-level algorithms, of whatever kind. The 
latter is certainly not an option yet. But as soon as such 
tools look simultaneously on written and compiled code new 
opportunities will arise.

rjf> On the other hand, if you have gobs of code written by high
rjf> school summer interns just learning Python, it is possible
rjf> their code is filled with non-idiomatic and inefficient 
rjf> constructions that might be detected/repaired.

The assumption that only the inexperienced beginner makes 
stupid mistakes or writes optimizable code is not likely to be 
shared here. But if, as you can seemingly imagine, such can 
be pointed to by checkers/optimizers, then the level of 
experience of the coder obviously does not matter.

Between these two extremes are the daily necessaries of the
programmer, and that is what it is all about. In this respect
I find Volker's answer informative and revealing at the same
time.

vb> First we should add one of the plain old rule-based linters
vb> to our testsuite, imho that would go a long way of maintaining
vb> a consistent code style (and avoid trivial review friction).

I have expected a lot of people answering Volker: "Hey, you
are right, after the next release let's introduce such tools
before we continue."

Since such answers did not come in I gladly admit to having
asked the question in the wrong place and at the wrong time.

The question for me remains whether this reluctance towards
such tools is typical for open-source/academic projects.
Interestingly also Volker writes only about 'code style', not
of other forms of improvements. I have the impression that
the situation in the industry is quite different.

rjf> It's your choice how to spend your time. Is it worth a try? 

You are absolutely right. I don't know any plausible estimate
that the programming time developers spend for debugging is 
less than 50%; many estimates are significantly higher. But
that's an old hat. 

P.

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