Let's introduce the problem :
Taking my role of "
That was the idea behind the teamlog2rss.py script I wrote last year. But now, going a bit further in the "big brother" concepts, I hacked a small EMF model to retrieve the logs from the team repository and to generate reports, I'm then able to analyze the log messages and file uri's :)
The model is straightforward, here is a quick extract of the result :
The model implementation is querying Subversive to retrieve the logs through a "derived reference". That solution is quite crappy as accessing the model means going through the network (meaning - that's slow), but the advantage is : my EMF model is a plain old EMF model, and as such any tool will work with it.
Then I'm producing reports using Acceleo, for each week and commiter, I'm checking:
- how many changes have been made
- how many unique files have been changed
- how many tests have been changed
- how many "happy checkstyle" commits were done
- commit activity concerning "bugfixing"
- how many "happy boyscout" commits were done
I'm then able to generate HTML, and even generate a small uri for Google charts to get nice pictures:
I'm even able to add the commiters picture in the "weekly hall of fame" through user code tags in the HTML :)
That said, M2T transformation is not the best pick for such a need, I really need to deduce new information from the original teamlog data to produce, afterwards, a nice report. Next try on this kata will probably involve M2M transformation to deduce the information, or Birt reporting... But, I don't want to spoil you, stay tuned ;)