slipcover
coveragepy
slipcover | coveragepy | |
---|---|---|
5 | 7 | |
404 | 2,831 | |
1.0% | - | |
9.0 | 9.6 | |
6 days ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
slipcover
- SlipCover: Near Zero-Overhead Python Code Coverage
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Slipcover: Near Zero-Overhead Python Code Coverage
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!
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This Week in Python
slipcover – Near Zero-Overhead Python Code Coverage
- GitHub - plasma-umass/slipcover: Near Zero-Overhead Python Code Coverage
coveragepy
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An Introduction to Testing with Django for Python
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.
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The Uncreative Software Engineer's Compendium to Testing
Code Coverage Analysis assess the code portions tested by the current test suites without altering the code.
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Slipcover: Near Zero-Overhead Python Code Coverage
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!
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Unit Tests - what’s the point?
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/
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How to make Django package smaller for Serverless deployment
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.
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Comparison of Python TOML parser libraries
coverage
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New Ways to Be Told That Your Python Code Is Bad
FWIW, ternary expressions aren't properly detected by coverage: https://github.com/nedbat/coveragepy/issues/509
What are some alternatives?
pyscript - Try PyScript: https://pyscript.com Examples: https://tinyurl.com/pyscript-examples Community: https://discord.gg/HxvBtukrg2
global-chem - A Knowledge Graph of Common Chemical Names to their Molecular Definition
datasette-lite - Datasette running in your browser using WebAssembly and Pyodide
Zappa - Serverless Python
django_channels_bingo_game - Real Time Multiplayer Bingo Game Using Django Channels and Javascript
pytomlpp - A python wrapper for tomlplusplus
django-readers - A lightweight function-oriented toolkit for better organisation of business logic and efficient selection and projection of data in Django projects.
flit - Simplified packaging of Python modules
scalene - Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
toml - Python lib for TOML
tomli - A lil' TOML parser
tomlplusplus - Header-only TOML config file parser and serializer for C++17.