Auto-tools/Projects/OrangeFactor/Statistics

War on Orange: Statistics

Are oranges getting better? Worse? How do we tell?

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

What are we really measuring? What does it mean? In order to combat the oranges, we must understand them. This means having in our arsenal creative measured quantities that tell a story.

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