Auto-tools/Projects/OrangeFactor/Statistics: Difference between revisions
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* oranges/push as a function of time | * oranges/push as a function of time | ||
* for a given window, breakdown of oranges by bug number | * for a given window, breakdown of oranges by bug number | ||
** in other words, are there a lot of different orange bugs? | |||
** is any of them a big chunk? | |||
** or are they highly scattered? | |||
* push rate (number of pushes as a function of time; pushes/week, etc) | * push rate (number of pushes as a function of time; pushes/week, etc) | ||
* occurance rate of orange bugs (per push) as a function of time | * occurance rate of orange bugs (per push) as a function of time | ||
Revision as of 22:48, 20 October 2010
War on Orange: Statistics
Types of Transforms
Given a time-series of data...
- filters: transform the series of data giving back the same number of points (as defined for this purpose)
- reductions: give back a scalar value, such as a mean, median, or standard deviation
- windows: take a subset of the window for further analysis
Note that a hg push series is a time series
We should move towards an architecture where an arbitrary set of filters may be applied. So you could e.g. filter, filter, window, reduce.
Interesting Statistics
Statistics should be identified that accurately and effectively convey trends in the data:
- oranges/push as a function of time
- for a given window, breakdown of oranges by bug number
- in other words, are there a lot of different orange bugs?
- is any of them a big chunk?
- or are they highly scattered?
- push rate (number of pushes as a function of time; pushes/week, etc)
- occurance rate of orange bugs (per push) as a function of time