nlohmannjson/test/thirdparty/fastcov

fastcov

A massively parallel gcov wrapper for generating intermediate coverage formats fast

The goal of fastcov is to generate code coverage intermediate formats as fast as possible (ideally < 1 second), even for large projects with hundreds of gcda objects. The intermediate formats may then be consumed by a report generator such as lcov's genhtml, or a dedicated front end such as coveralls. fastcov was originally designed to be a drop-in replacement for lcov (application coverage only, not kernel coverage).

Currently the only intermediate formats supported are gcov json format and lcov info format. Adding support for other formats should require just a few lines of python to transform gcov json format to the desired shape.

In order to achieve the massive speed gains, a few constraints apply:

1. GCC version >= 9.0.0

These versions of GCOV have support for JSON intermediate format as well as streaming report data straight to stdout

2. Object files must be either be built:

• Using absolute paths for all -I flags passed to the compiler
• Invoking the compiler from the same root directory

If you use CMake, you are almost certainly satisfying the second constraint (unless you care about ExternalProject coverage).

Sample Usage:

$ cd build_dir
$ fastcov.py --zerocounters
$ <run unit tests>
$ fastcov.py --exclude /usr/include --lcov -o report.info
$ genhtml -o code_coverage report.info

Legacy fastcov

It is possible to reap most of the benefits of fastcov for GCC version < 9.0.0 and >= 7.1.0. However, there will be a potential header file loss of correctness.

fastcov_legacy.py can be used with pre GCC-9 down to GCC 7.1.0 but with a few penalties due to gcov limitations. This is because running gcov in parallel generates .gcov header reports in parallel which overwrite each other. This isn't a problem unless your header files have actual logic (i.e. header only library) that you want to measure coverage for. Use the -F flag to specify which gcda files should not be run in parallel in order to capture accurate header file data just for those. I don't plan on supporting fastcov_legacy.py aside from basic bug fixes.

Benchmarks

Anecdotal testing on my own projects indicate that fastcov is over 100x faster than lcov and over 30x faster than gcovr:

Project Size: ~250 .gcda, ~500 .gcov generated by gcov

Time to process all gcda and parse all gcov:

• fastcov: ~700ms
• lcov: ~90s
• gcovr: ~30s