Downloading starred emails from GMail to apply git patches

Now that RTEMS is using git I frequently have to apply patches received by email, but GMail lacks a web interface for downloading selected emails. The conventional wisdom for how to apply a patch from an email is to "show original", save the page, manually remove the first blank line, and then use git-am to apply. Now do that ten times for a series of patches. How tedious!

So I wrote fetch-flagged-email—a small Python application—to accomplish my goal. Now I star patches in gmail and then run fetch-flagged-email.py followed by git-am. The script does the heavy lifting of saving emails to files in sequential order to a directory of my choosing.

productivity++;

Crufty Stuff

Joel Sherrill posted back in 2010 about the lines of code in RTEMS. I was curious about something related to this so first I replicated his work using sloccount. I think he may have neglected the inline files (because I had to add the .inl extension). I also added support for m4, .ac, and .am files. The latter two are part of the autotools build system and are filed under 'makefile'. Here are the current results on the RTEMS tree

Totals grouped by language (dominant language first):
ansic:       745153 (86.51%)
asm:          38574 (4.48%)
makefile:     34934 (4.06%)
ada:          26261 (3.05%)
sh:            7086 (0.82%)
cpp:           5248 (0.61%)
m4:            2996 (0.35%)
pascal:         814 (0.09%)
perl:           279 (0.03%)

Total SLOC  861,345

Now for the interesting part: I also ran sloccount after running bootstrap, which generates a bunch of files for the build system. I modified sloccount to also count the Makefile.in files (which are generated with autotools from Makefile.am sources). Here are the results

ansic:       745153 (35.73%)
sh:          638471 (30.61%)
makefile:    557257 (26.72%)
m4:           73475 (3.52%)
asm:          38574 (1.85%)
ada:          26261 (1.26%)
cpp:           5248 (0.25%)
pascal:         814 (0.04%)
perl:           279 (0.01%)

SLOC*     2,085,532
* includes generated files

And do you know how many humans look at that generated code? Any idea how much of it is tested?

It would be interesting to include all the other code that goes into making RTEMS work such as the compiler, RPM packing scripts, and so on. The moral of this data is that the build system is complex. Very complex.

This data was generated using David A. Wheeler's 'SLOCCount'.