Hi Dave and the NbGallery team.

I'll add this on our to-read list ! I think that this might be of interest
for JupyteCon, the Call for Proposal was opened last week:
https://blog.jupyter.org/jupytercon-2018-call-for-proposal-87986014ee0b

Happy to also see some ruby notebooks !

Nice work! Thanks,
-- 
Matthias

On 23 January 2018 at 12:06, Dave <[email protected]> wrote:

> Hi All,
>
> I wanted to share the following white paper
> <https://nbgallery.github.io/health_paper.html> (abstract below) which
> details our team's work to systematically measure the code health of
> Jupyter notebooks from within an nbgallery instance.  As background, nbgallery
> <https://github.com/nbgallery/nbgallery>is an enterprise Jupyter notebook
> sharing and collaboration platform developed within the Department of
> Defense.  Our team operates in a unique environment which requires us to
> take some interesting and unconventional approaches to evaluating code
> health.  For instance, traditional unit testing in our environment is a
> challenge since we use dynamic data sets with row-level security (so the
> data landscape shifts by the day and by the user), and many of our notebook
> contributors are not typical software developers with experience or
> interest in developing unit tests.  We think some of our practical
> approaches arising from evaluating code health in a complex notebook
> environment might extend to projects like JupyterHub and Binder.
>
> Please check out the paper <https://nbgallery.github.io/health_paper.html>
> and let us know if you have any questions/comments.
>
> Thanks!
>
> Dave
>
> *Systems that support user-developed code are faced with a key challenge:
> understanding the health of that code, which we define as the expectation
> that existing code will function properly in the current environment. The
> growing popularity of Jupyter notebooks has led to the development of
> publishing and execution platforms such as the open-source nbgallery
> <https://github.com/nbgallery/nbgallery> project. Users of nbgallery would
> like to understand when they can expect a notebook to work, and notebook
> authors may wish to monitor the execution of their code and be informed of
> errors. This paper describes our initial efforts to measure code health in
> a corpus of notebooks within an instance of nbgallery. Our vision is that
> this work will help address problems that arise from user-developed code
> and motivate further study in systems beyond Jupyter and nbgallery.*
>
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