In a bored 5 minutes at work I threw the following together: Logical source file groupings in the Scala repo
The largest cluster is clearly noisy and random. I more or less expected that. But the small and medium ones often make a lot of sense.
The basic technique is straightforward: We use a trivial script to scrape SVN logs to get a list of files that change in each commit. We use this to calculate the binary pearsons of these observations to get a measure of the similarity between two files (a number between -1 and 1, though we throw away anything <= 0). We then use markov clustering to cluster the results into distinct groupings.
The results are obviously far from perfect. But equally obviously there’s a lot of interesting information in them, and the technique could certainly be refined (e.g. by looking at sizes of diffs on each file and using that rather than a simple 0/1 changed. Also experimenting with other clustering algorithms, etc). Maybe something worth pursuing?
