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林泽燕 wrote:
> Dear everyone,
> 
> My name is Zoey Lin, majored in Computer Science, Peking University,
> China. I’m a candidate of Master Degree. Recently I'm making a research
> on OpenStack about the contribution composition of a code file, to
> predict the potential amount of defect that the file would have in the
> later development stage of a release.
> 
> I wonder if I could show you my study, including some metrics for the
> prediction model and a visualization tool. I would appreciate it if you
> could share your opinions or give some advices, which would really,
> really help me a lot. Thank you so much for your kindness. :)
> [...]

I'd like to echo what Jeremy said and thank you for your insightful
research. I've been interested in using risk prediction and machine
learning as a part of our review process to increase quality.

Your scientific analysis seems to match what we intuitively know: larger
files will contain more bugs than smaller files, and (beyond a few
outliers), complex files which see lots of contributions will trigger
more issues than simple files that only needed to be written once. So
I'm wondering how much of that feedback can be used to improve the code:
I think we internalize most of that risk assessment already.

One insight which I think we could take from this is that when a smaller
group of people "owns" a set of files, we raise quality (compared to
everyone owning everything). So the more we can split the code along
areas of expertise and smaller review teams, the better. But I think
that is also something we intuitively knew.

Regards,

-- 
Thierry Carrez (ttx)