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• FFmpeg and Code Coverage Tools

  21 août 2010, par Multimedia Mike — FATE Server, Python

  Code coverage tools likely occupy the same niche as profiling tools : Tools that you’re supposed to use somewhere during the software engineering process but probably never quite get around to it, usually because you’re too busy adding features or fixing bugs. But there may come a day when you wish to learn how much of your code is actually being exercised in normal production use. For example, the team charged with continuously testing the FFmpeg project, would be curious to know how much code is being exercised, especially since many of the FATE test specs explicitly claim to be "exercising XYZ subsystem".

  The primary GNU code coverage tool is called gcov and is probably already on your GNU-based development system. I set out to determine how much FFmpeg source code is exercised while running the full FATE suite. I ran into some problems when trying to use gcov on a project-wide scale. I spackled around those holes with some very ad-hoc solutions. I’m sure I was just overlooking some more obvious solutions about which you all will be happy to enlighten me.

  Results
  I’ve learned to cut to the chase earlier in blog posts (results first, methods second). With that, here are the results I produced from this experiment. This Google spreadsheet contains 3 sheets : The first contains code coverage stats for a bunch of FFmpeg C files sorted first by percent coverage (ascending), then by number of lines (descending), thus highlighting which files have the most uncovered code (ffserver.c currently tops that chart). The second sheet has files for which no stats were generated. The third sheet has "problems". These files were rejected by my ad-hoc script.

  Here’s a link to the data in CSV if you want to play with it yourself.

  Using gcov with FFmpeg
  To instrument a program for gcov analysis, compile and link the target program with the -fprofile-arcs and -ftest-coverage options. These need to be applied at both the compile and link stages, so in the case of FFmpeg, configure with :

    ./configure \
      —extra-cflags="-fprofile-arcs -ftest-coverage" \
      —extra-ldflags="-fprofile-arcs -ftest-coverage"
  

  The building process results in a bunch of .gcno files which pertain to code coverage. After running the program as normal, a bunch of .gcda files are generated. To get coverage statistics from these files, run 'gcov sourcefile.c'. This will print some basic statistics as well as generate a corresponding .gcov file with more detailed information about exactly which lines have been executed, and how many times.

  Be advised that the source file must either live in the same directory from which gcov is invoked, or else the path to the source must be given to gcov via the '-o, --object-directory' option.

  Resetting Statistics
  Statistics in the .gcda are cumulative. Should you wish to reset the statistics, doing this in the build directory should suffice :

    find . -name "*.gcda" | xargs rm -f
  

  Getting Project-Wide Data
  As mentioned, I had to get a little creative here to get a big picture of FFmpeg code coverage. After building FFmpeg with the code coverage options and running FATE,

  for file in `find . -name "*.c"` \
  do \
    echo "*****" $file \
    gcov -o `dirname $file` `basename $file` \
  done > ffmpeg-code-coverage.txt 2>&1
  

  After that, I ran the ffmpeg-code-coverage.txt file through a custom Python script to print out the 3 CSV files that I later dumped into the Google Spreadsheet.

  Further Work
  I’m sure there are better ways to do this, and I’m sure you all will let me know what they are. But I have to get the ball rolling somehow.

  There’s also TestCocoon. I’d like to try that program and see if it addresses some of gcov’s shortcomings (assuming they are indeed shortcomings rather than oversights).

  Source for script : process-gcov-slop.py

  PLAIN TEXT

  PYTHON :

  1. # !/usr/bin/python
  2.  
  3. import re
  4.  
  5. lines = open("ffmpeg-code-coverage.txt").read().splitlines()
  6. no_coverage = ""
  7. coverage = "filename, % covered, total lines\n"
  8. problems = ""
  9.  
  10. stats_exp = re.compile(’Lines executed :(\d+\.\d+)% of (\d+)’)
  11. for i in xrange(len(lines)) :
  12.   line = lines[i]
  13.   if line.startswith("***** ") :
  14.     filename = line[line.find(’./’)+2 :]
  15.     i += 1
  16.     if lines[i].find(":cannot open graph file") != -1 :
  17.       no_coverage += filename + ’\n’
  18.     else :
  19.       while lines[i].find(filename) == -1 and not lines[i].startswith("***** ") :
  20.         i += 1
  21.       try :
  22.         (percent, total_lines) = stats_exp.findall(lines[i+1])[0]
  23.         coverage += filename + ’, ’ + percent + ’, ’ + total_lines + ’\n’
  24.       except IndexError :
  25.         problems += filename + ’\n’
  26.  
  27. open("no_coverage.csv", ’w’).write(no_coverage)
  28. open("coverage.csv", ’w’).write(coverage)
  29. open("problems.csv", ’w’).write(problems)

• Revision 25251 : correction de [25196] NK

  17 décembre 2008, par nicolas1.krebs2@… — Log

  correction de [25196] NK

• Introducing WebM, an open web media project

  20 mai 2010, par noreply@blogger.com (christosap)

  A key factor in the web’s success is that its core technologies such as HTML, HTTP, TCP/IP, etc. are open and freely implementable. Though video is also now core to the web experience, there is unfortunately no open and free video format that is on par with the leading commercial choices. To that end, we are excited to introduce WebM, a broadly-backed community effort to develop a world-class media format for the open web.

  WebM includes :
  

  • VP8, a high-quality video codec we are releasing today under a BSD-style, royalty-free license
  • Vorbis, an already open source and broadly implemented audio codec
  • a container format based on a subset of the Matroska media container

  The team that created VP8 have been pioneers in video codec development for over a decade. VP8 delivers high quality video while efficiently adapting to the varying processing and bandwidth conditions found on today’s broad range of web-connected devices. VP8’s efficient bandwidth usage will mean lower serving costs for content publishers and high quality video for end-users. The codec’s relative simplicity makes it easy to integrate into existing environments and requires less manual tuning to produce high quality results. These existing attributes and the rapid innovation we expect through the open-development process make VP8 well suited for the unique requirements of video on the web.

  A developer preview of WebM and VP8, including source code, specs, and encoding tools is available today at www.webmproject.org.

  We want to thank the many industry leaders and web community members who are collaborating on the development of WebM and integrating it into their products. Check out what Mozilla, Opera, Google Chrome, Adobe, and many others below have to say about the importance of WebM to the future of web video.

  
  

  AMD

  ARM

  Brightcove

  Broadcom

  Collabora

  Digital Rapids

  Encoding.com

  Grab Networks

  iLinc

  INLET

  Kaltura

  Logitech

  MIPS

  Nvidia

  Ooyala

  Qualcomm

  Skype

  Sorenson

  Telestream
  

  Texas Instruments

  Verisilicon

  ViewCast

  Wildform

  
  

  Posted by Jeremy Doig, Engineering Director of video and Mike Jazayeri, Group Product Manager, Google