coveragepy
bandit
coveragepy | bandit | |
---|---|---|
7 | 21 | |
2,838 | 6,008 | |
- | 1.6% | |
9.6 | 8.2 | |
about 22 hours 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.
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
bandit
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Enhance Your Project Quality with These Top Python Libraries
Bandit is a tool designed to find common security issues in Python code. It was developed by the OpenStack Security Project and is a great addition to any serious Python project.
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Creating a DevSecOps pipeline with Jenkins — Part 1
For the SAST stage, I used SonarQube tool. SonarQube is an open-source platform developed by SonarSource for continuous inspection of code quality to perform automatic reviews with static analysis of code to detect bugs and code smells on more than 30 programming languages. I preferred SonarQube instead of other SAST tools because it has a detailed documentation and plugins about integration with Jenkins and SonarQube works with Java projects pretty well. Of course you can similar multi-language-supported tools such as Semgrep or language-specific tools such as Bandit.
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Enhance your python code security using bandit
repos: - repo: https://github.com/PyCQA/bandit rev: 1.7.7 hooks: - id: bandit args: ["-c", "pyproject.toml", "-r", "."] additional_dependencies: ["bandit[toml]"]
- Show HN: Codemodder – A new codemod library for Java and Python
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
On the other hand, Bandit is a dedicated security scanner designed to target critical security concerns such as SQL injection and cross-site scripting exploits. It meticulously scrutinizes the codebase to identify and alert developers about possible security breaches or vulnerabilities, thus fortifying the code against potential exploitation.
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The Uncreative Software Engineer's Compendium to Testing
Bandit: is a tool designed for Python applications to analyse your code for potential security issues like insecure use of functions, hardcoded password and much more.
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The 36 tools that SaaS can use to keep their product and data safe from criminal hackers (manual research)
Bandit (for Python, open-source and free)
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Which CI/CD learn first?
Add security checks (Bandit) and dependency checks (safety)
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Why are python coding standards such a mess, what is everything and where do I start?
bandit
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Python toolkits
flake8-bandit which uses bandit for security linting.
What are some alternatives?
global-chem - A Knowledge Graph of Common Chemical Names to their Molecular Definition
Flake8 - flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
slipcover - Near Zero-Overhead Python Code Coverage
pre-commit-hooks - Some out-of-the-box hooks for pre-commit
Zappa - Serverless Python
safety - Safety checks Python dependencies for known security vulnerabilities and suggests the proper remediations for vulnerabilities detected.
pytomlpp - A python wrapper for tomlplusplus
flake8-bandit - Automated security testing using bandit and flake8.
flit - Simplified packaging of Python modules
black - The uncompromising Python code formatter
toml - Python lib for TOML
mypy - Optional static typing for Python