The chance that Machine Learning AI programs will find important algorithmic efficiency fixes in algebraic mathematical implementations seems pretty slim. On the other hand, if you have gobs of code written by high school summer interns just learning Python, it is possible their code is filled with non-idiomatic and inefficient constructions that might be detected/repaired.
It's your choice how to spend your time. Is it worth a try? RJF On Tuesday, May 1, 2018 at 4:36:17 AM UTC-7, Volker Braun wrote: > > First we should add one of the plain old rule-based linters to our > testsuite, imho that would go a long way of maintaining a consistent code > style (and avoid trivial review friction). > > > > On Sunday, April 29, 2018 at 3:43:23 PM UTC+2, Peter Luschny wrote: >> >> Hi, >> >> does such a thing make sense for use in the SageMath development? >> >> The website says: Free Forever for GitHub open source. >> >> >> https://techcrunch.com/2018/04/26/deepcode-cleans-your-code-with-the-power-of-ai/ >> >> P. >> >> -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/sage-devel. For more options, visit https://groups.google.com/d/optout.
