Some while back I had started thinking about automated ways analyze telemetry data and it occurred to me that maybe we should be looking at independent variables other than time. That is, we seem to look at some metric vs. time for the most part. This time series view is very tough to analyze and especially automate the recognition of interesting patterns. While dozing off at a conference last week something hit me (no, not my neighbor waking me up). What if we transformed the time-series telemetry data stream into.... a frequency-series. That is, do a FFT on the data. This would be like looking at the frequency spectrum of an audio stream (although MUCH simpler).
I am extremely naive about FFT, but my sense is that this approach basically converts a telemetry stream into a 'fingerprint' which is based upon oscillations. My hypothesis is that this fingerprint could represent, in some sense, a development 'process'.
If that's so, then the next set of questions might be:
- Is this 'process' representation stable? Would we get the same/similar FFT later or from a different group?
- Is this process representation meaningful? Are the oscillations indicative of anything useful/important about the way people work? Does this depend upon the actual kind of telemetry data that is collected?
1 comment:
This could measure the "frequency" of various events (i.e. code check in at once/day, while compiles at 20/day, etc.). Then maybe we could correlate this across project life cycle, or engineer experience, etc. Sounds like this may be able to uncover some interesting information. I notice that my frequency code development check-ins have dropped significantly since taking Software Engineering :).
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