--- Comment #15 from Quim Gil <q...@wikimedia.org> ---
Status update (Nemo, I will leave your comment to Alvaro since I don't know
We have some graphs here:
"How long is the Gerrit review queue over time?" and "Backlog of pending
reviews (when reviews were submitted)" could be put together, both starting
when Gerrit was set up (march 2012?).
Maybe they could even be combined in a single graph? (optional)
Little known bug: all graphs should have Y axis starting with 0.
"How long does it take to review code contributions?" and "Are Wikimedia's
staff and non-staff contributions processed equally?" also belong to each
other. At the end the second question equals "How long does it take to review
code contributions... from WMF, independents, WMDE, Wikia...?"
We are still discussing what to count exactly here. We started the discussion
in our weekly meeting and we will continue here. The last assumption (which is
not reflected in the graphs now) is:
For each measurement timespot, median of the ages of all reviews still open at
that timespot, understanding as "age" the time period since the review
was open to the measurement timespot.
Note that a review can have multiple patches. I'm proposing to count the age of
the last patch instead of the age of creation of the review. What we are most
interested in seeing is lack of activity, languishing reviews. Counting the age
since creation is also useful, as secondary value (a second graph?)
"Who is doing better and worse?" should be sorted by median age of open
reviews, starting by those repos with oldest patches awaiting review.
In our discussion there were more ideas about data we could count. I'm
documenting them here, although this might be relegated to a second iteration
(since we have other KPIs that we want to complete in the following weeks):
* median time between uploading the changeset and receiving a first review
* average of iterations vs time since creation (some changesets may take long
to review but they were nailed in a couple of revisions as soon as someone
answered, while others required many iterations)
* normalization of data for big projects, calculating number of revisions
within a period compared with the previous period.
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