tomlplusplus VS coveragepy

Unless you want to make your own config file parser as an exercise (which is a good idea) I'd recommend using toml++.

It's a public repo - this one: https://github.com/marzer/tomlplusplus but thank you

For a live demo, see the toml++ homepage: https://marzer.github.io/tomlplusplus/

Added bonus, tomlplusplus is a solid C++ implementation, actively updated, and easy to drop into a project (header only, CMake, vcpkg, Conan, etc.). Very easy to use for what I've needed.

Key links: - Homepage: https://marzer.github.io/tomlplusplus/ - Repository: https://github.com/marzer/tomlplusplus - "What is TOML?": https://toml.io/

cmake_minimum_required(VERSION 3.23) project(testProj) set(CMAKE_CXX_STANDARD 23) set(FETCHCONTENT_BASE_DIR ${PROJECT_SOURCE_DIR}/${CMAKE_PROJECT_NAME}/libs CACHE PATH "Missing description." FORCE) include(FetchContent) FetchContent_Declare( tomlplusplus GIT_REPOSITORY https://github.com/marzer/tomlplusplus.git GIT_TAG v3.1.0 ) FetchContent_MakeAvailable(tomlplusplus) add_executable(testProj testProj/src/main.cpp) target_link_libraries(testProj tomlplusplus)

I use them in toml++ to store the path of the source document for all the data nodes parsed from that document. Storing it as a separate std::string allocation for every single object would have been very wasteful, and the alternative would have been to make an overall document class that owned everything, which I didn't want to do, so shared_ptr was a good compromise.

Key links: - Homepage: https://marzer.github.io/tomlplusplus/ - Repository: https://github.com/marzer/tomlplusplus - "What is TOML?": https://toml.io/

toml++ was both my first 'public'/open-source project, and my first ever attempt at a parser. There's a few things I'd do differently now if I were to write it from scratch, but I've learned a lot along the way (particularly about making things build 'clean' on multiple platforms).

Coverage.py is the go-to tool for measuring code coverage of Python programs. Once installed, you can use it with either unittest or pytest.

Code Coverage Analysis assess the code portions tested by the current test suites without altering the code.

The PLASMA lab @ UMass Amherst (home of the Scalene profiler) has released a new version of Slipcover, a super fast code coverage tool for Python. It is by far the fastest code coverage tool: in our tests, its average slowdown is just 5% (compare to the widely used coverage.py, average slowdown 218%!). The latest release performs both line and branch coverage with virtually no overhead. Use it to dramatically speed up your tests and continuous integration!

Tests ensure the tested behavior is maintained. It's up to the developers to write tests with sufficient coverage. Determining which lines of code on your project are covered by tests is easily quantifiable using tooling. E.g. https://coverage.readthedocs.io/

Taking the idea further, if you build robust tests for your API, you could use a dynamic code analyzer like coverage or figleaf to identify and delete unused functions.

coverage

FWIW, ternary expressions aren't properly detected by coverage: https://github.com/nedbat/coveragepy/issues/509