Re: [PATCH] (experimental) per-topic shortlog.

From: Linus Torvalds <torvalds@osdl.org>
Date: Nov 27, 2006, 01:06 UTC
Message-ID: <Pine.LNX.4.64.0611261652520.30076@woody.osdl.org>
In-Reply-To: <7v8xhxsopp.fsf@assigned-by-dhcp.cox.net>

On Sun, 26 Nov 2006, Junio C Hamano wrote:

Show 7 quoted lines

>
> This implements an experimental "git log-fpc" command that shows
> short-log style output sorted by topics.
> 
> A "topic" is identified by going through the first-parent
> chains; this ignores the fast-forward case, but for a top-level
> integrator it often is good enough.

Umm. May I suggest that you try this with the kernel repo too..

There, the "first parent chain" tends to be less interesting than a lot of other heuristics:

 - committer

   If the committer changes, you should probably consider it a break, the 
   same way a second parent would be a break. You probably won't see this 
   in the git archive, because there tends to be a single committer, but 
   on something like the kernel where we really merge other peoples repos, 
   it's going to be as good (or better) than looking at "other parents".

 - subdirectory heuristics

   Again, with git it's not very interesting, but I bet that you'd be able 
   to use heuristics like "the bulk of the changes were contained within 
   this directory tree" for projects like the kernel, and automatically 
   decide on "topics" like drivers/scsi, fs/ext3 etc.

In other words, I don't think the "fpc" decision is even very interesting. If you _really_ want to do a cool shortlogger, I bet it can be done, but I suspect that it would be a LOT cooler to do some automatic bayesian clustering based on committer, author and list of filenames changed.

Of course, such a thing done well would probably be worthy of a doctoral thesis or something. Maybe somebody on this list who is into bayesian clustering and doesn't have a thesis subject...

(Of course, since I haven't been in a University setting for the last ten years, maybe bayesian clustering isn't the cool thing to work on any more).

Anyway, "topics" really should be something that is extremely open to various clustering models, bayesian or not ..