Application usage data is one of the rarest kinds of public software engineering data.
Even data that might be used to approximate application usage is rare. Server logs might be used as a proxy for browser usage or operating system usage, and number of Debian package downloads as a proxy for usage of packages.
Usage data is an important component of fault prediction models, and the failure to incorporate such data is one reason why existing fault models are almost completely worthless.
The paper Deriving a Usage-Independent Software Quality Metric appeared a few months ago (it’s a bit of a kitchen sink of a paper), and included lots of usage data! As far as I know, this is a first.
The data relates to a mobile based communications App that used Google analytics to log basic usage information, i.e., daily totals of: App usage time, uses by existing users, uses by new users, operating system+version used by the mobile device, and number of exceptions raised by the App.
Working with daily totals means there is likely to be a non-trivial correlation between usage time and number of uses. Given that this is the only public data of its kind, it has to be handled (in my case, ignored for the time being).
I’m expecting to see a relationship between number of exceptions raised and daily usage (the data includes a count of fatal exceptions, which are less common; because lots of data is needed to build a good model, I went with the more common kind). So a’fishing I went.
On most days no exception occurred (zero is the ideal case for the vendor, but I want lots of exception to build a good model). Daily exception counts are likely to be small integers, which suggests a Poisson error model.
It is likely that the same set of exceptions were experienced by many users, rather like the behavior that occurs when fuzzing a program.
Applications often have an initial beta testing period, intended to check that everything works. Lucky for me the beta testing data is included (i.e., more exceptions are likely to occur during beta testing, which get sorted out prior to official release). This is the data I concentrated my modeling.
The model I finally settled on has the form (code+data):
Yes, had a much bigger impact than . This was true for all the models I built using data for all Android/iOS Apps, and the exponent difference was always greater than two.
Why square-root, rather than log? The model fit was much better for square-root; too much better for me to be willing to go with a model which had as a power-law.
The impact of varied by several orders of magnitude (which won’t come as a surprise to developers using earlier versions of Android).
There were not nearly as many exceptions once the App became generally available, and there were a lot fewer exceptions for the iOS version.
The outsized impact of new users on exceptions experienced is easily explained by developers failing to check for users doing nonsensical things (which users new to an App are prone to do). Existing users have a better idea of how to drive an App, and tend to do the kind of things that developers expect them to do.
As always, if you know of any interesting software engineering data, please let me know.