Open xiaoya-yaya opened 3 years ago
This issue has not been replied for 24 hours, please pay attention to this issue: @sunshinemingo @wengzhenjie
I can not see what to implement in this metric, the charts just show the event count and the burstiness is just a sense of the smoothness of the charts, is it right? @xiaoya-yaya
I think this metric provides a perspective for maintainers to observe: to find the peaks of the number of commits, comments, downloads, forks, etc, and then to find connections of facts that are related to the burstiness (release, meetups, etc).
The observation could be just based on the observer's sense, at least CHAOSS didn't provide specific methods. However, there are some peak detection methods integrated into python or javascript packages. I know scipy
have signal.find_peaks()
function to identify peaks in curves.
Thanks for the input, I found a blog about scipy.signal.find_peaks
and it gives a corresponding JavaScript implementation.
According to the blog, I think peak detection can be a serious research problem and there are several parameters in the function, so if we want to implement this metric, we may need to look into the peak detection algorithm more carefully.
But it is truly a good metric to build a monitor-and-alert system for other metrics.
I am quite curious about the predictability of those signal features:
Is there any set of metrics exists like this?
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