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47 lines
2.4 KiB
Markdown
47 lines
2.4 KiB
Markdown
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# fastcov
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A massively parallel gcov wrapper for generating intermediate coverage formats *fast*
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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).
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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.
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In order to achieve the massive speed gains, a few constraints apply:
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1. GCC version >= 9.0.0
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These versions of GCOV have support for JSON intermediate format as well as streaming report data straight to stdout
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2. Object files must be either be built:
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- Using absolute paths for all `-I` flags passed to the compiler
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- Invoking the compiler from the same root directory
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If you use CMake, you are almost certainly satisfying the second constraint (unless you care about `ExternalProject` coverage).
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## Sample Usage:
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```bash
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$ cd build_dir
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$ fastcov.py --zerocounters
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$ <run unit tests>
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$ fastcov.py --exclude /usr/include --lcov -o report.info
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$ genhtml -o code_coverage report.info
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```
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## Legacy fastcov
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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.
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`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.
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## Benchmarks
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Anecdotal testing on my own projects indicate that fastcov is over 100x faster than lcov and over 30x faster than gcovr:
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Project Size: ~250 .gcda, ~500 .gcov generated by gcov
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Time to process all gcda and parse all gcov:
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- fastcov: ~700ms
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- lcov: ~90s
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- gcovr: ~30s
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