Gcovr 4.2 is out, and provides a broad array of new features! With the ability to use config files, new output formats like JSON and SonarQube XML, and the ability to combine coverage data from multiple runs, this release should make it easier to create coverage report in more complex scenarios.

Gcovr is a command line tool that uses gcov to produce code coverage reports in various formats, such as text summaries, detailed HTML reports, and various machine-readable formats. It works with the GCC and Clang compilers.

You can pip install gcovr from PyPI, read the overview on GitHub, or read the full documentation.

After a lot of refactoring and cleanup, gcovr 4.0 is out!

This release represents four months of work. The focus was on internal changes, but there are lots of enhancements as well: better filters, improved Python 3 support, GCC 8 support, better encoding support via --source-encoding, parallel gcov invocations with -j, and HTML customizations with --html-title, --html-medium-threshold, and --html-high-threshold.

Gcovr is a command line tool that uses gcov to produce code coverage reports as text summaries, detailed HTML reports, or Cobertura-compatible XML. I works with the GCC and Clang compilers.

You can pip install gcovr from PyPI, read the overview on GitHub, or read the user guide.

I am excited to announce that gcovr 3.4 has been released!

This release represents over a year of accumulated bug fixes. Support for Windows and for Python 3 has been greatly improved. There are also a number of quality improvements behind the scenes.

Gcovr is a command line tool that uses gcov to produce code coverage reports as text summaries, detailed HTML reports, or Cobertura-compatible XML. I works with the GCC and Clang compilers.

You can pip install gcovr from PyPI, read the overview on GitHub, or read the user guide.

A few days ago, I was trying to split a data set into various files according to their type, but it was terribly slow. I saved an hour by taking a step back and thinking about my algorithms. Because algorithms matter. But not terribly much.

Using the recurrence relation \binom n m = \binom {n - 1} {m - 1} + \binom {n - 1} m, we develop a dynamic programming algorithm to calculate the binomial coefficient. I am aware that better algorithms exist.