Python software development best practices resources.
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Updated
Jul 18, 2018
Automate your code review with style, quality, security, and test‑coverage checks when you need them most. Code quality is intended to keep complexity down and runtime up.
Python software development best practices resources.
@rakanfalroba (راكان بن فريح الربع)
To analysis and improve php project source code
Managing code quality in Node App with Linter tool
A reimplementation of Queen's Computing CISC 327 Fall 2018 course project for fun and practice
SourceGuard is designed to leverage Check Point's varied prevention technologies and services, providing source-code security and visibility. With a simple, cross-platform CLI tool users can customize exclusions and control an ignore list with easy integration into any pipeline.
A GitHub Action for detecting specified languages (e.g., Chinese or Japanese) in comments within code files across multiple programming languages (YAML, Go, Java, Rust). Ideal for projects aiming to adhere to internationalization standards or maintain language-specific coding guidelines.
Testing various methods of defining and usage of Constants in Java
Turn that misspell report into a GitLab compatible Code Climate report
Docker container using unityci/editor with .NET SDK to run dotnet commands in Unity environment
This is a simple code for avoiding console shaming
🧹 A one-stop ESLint plugin inspiring cleaner, more elegant code. Simplify linting with a comprehensive solution.
Professional grade prettier configuration
Data visualization for JSCPD report
CodeGuardAI is an application designed to enhance the code review process by automatically checking uncommitted changes and utilizing OpenAI's powerful code analysis capabilities to determine if the code is up to standard or requires improvements.
Max3Sat problem solution! 💻
Souvent trompeur les acronymes dans le code n'apporte que de la complexité pour vous et pour les futures devs sur le projet
Resources for online course Python code quality created in 2024 for Skillmea.sk portal.
State-of-art style guide for medium-to-huge repositories and diverse teams of engineers.
Instant AI code review for any PR
Automatically fix pull requests to increase developer productivity
Automatic cleaning of code
Ensure that new code is fully covered, and see coverage trends emerge. Works with any CI service
a continuous integration service for the pre-commit framework
an add-on to github actions which allows safely pushing changes back to pull requests
Automated code reviews to help developers ship better software, faster
A software engineering intelligence tool tool to identify and prioritize technical debt and evaluate your efficiency
Pull request metrics, daily activity summaries, and custom alerts
Automatic test report merging for all CI and languages into a single code coverage report directly into your pull request
CommitCheck ensures your commit messages are consistent and contain all required information
Advanced static analysis for automatically finding runtime errors in JavaScript code
We help developers write clean code
Automated code review for technical debt and test coverage
Apply size labels to Pull Requests based on the total lines of code changed