Pyscn - An AI-powered code quality analysis tool for quickly identifying and cleaning up code issues.

What is PySCN?

Pyscn is an intelligent code quality analysis tool designed specifically for Python developers. It helps developers quickly identify and clean up code issues such as dead code, duplicate code, and complex code structures. It detects dead code using Control Flow Graph (CFG), identifies code clones using tree edit distance technology, and analyzes code coupling and cyclomatic complexity. Pyscn boasts a fast analysis speed of 100,000+ lines per second and supports multiple installation methods, including pipx and Go installations. Pyscn provides comprehensive analysis capabilities, supports generating HTML and JSON reports, and allows users to customize analysis rules through configuration files. It also supports CI/CD integration, facilitating use in continuous integration environments.

PySCN's main functions

• Dead code detectionBy analyzing control flow graphs (CFGs), we can quickly identify dead code that is unreachable in the code, helping to clean up redundant code.
• Code clone detectionUsing tree edit distance (APTED) and locality-sensitive hashing (LSH) techniques, it identifies duplicate or similar code snippets and suggests refactoring opportunities.
• Coupling metricsCalculates the coupling degree between classes (CBO), helping developers monitor code architecture and optimize module dependencies.
• Cyclomatic complexity analysisAnalyze the cyclomatic complexity of functions, identify functions with excessive complexity, and suggest splitting them to improve code readability and maintainability.
• Quick Analysis and ReportingIt supports generating analysis reports in HTML and JSON formats, providing a clear view of code quality issues.
• Flexible configurationIt provides configuration files, allowing users to customize analysis rules to meet the needs of different projects.
• CI/CD IntegrationIt supports integration with tools such as GitHub Actions and pre-commit, making it convenient to use in continuous integration environments.

PySCN's technical principles

• Control Flow Graph (CFG) AnalysisBy constructing a control flow graph of the code, unreachable code can be detected and dead code can be identified.
• Tree edit distance (APTED) and locality-sensitive hashing (LSH)Combining these two techniques, we can detect the similarity of code snippets and identify duplicate code.
• Coupling metric (CBO)Calculate the coupling degree between classes to help analyze the modularity of the code.
• Cyclone complexity calculationAnalyze the complexity of the logical branches in the code and evaluate the complexity of the function.
• High-performance implementationBased on the Go language and tree-sitter technology, it enables fast code parsing and analysis, and supports efficient processing of large amounts of code.

Pyscn project address

• Github repositoryhttps://github.com/ludo-technologies/pyscn

Application scenarios of PySCN

• Rapid development phaseIn the pursuit of rapid iteration and efficient development, it helps developers to promptly identify and clean up problems in the code, maintaining code quality.
• Code maintenance and refactoringIt identifies duplicate code, dead code, and overly complex code, providing a basis for code refactoring and optimizing code structure.
• Team Collaboration DevelopmentIn collaborative projects, it is essential to maintain consistent code quality standards to ensure that code submitted by team members meets quality requirements.
• Teaching and LearningAs a teaching tool, it helps students and novice developers understand code quality issues and improve their coding skills.
• Enterprise-level projectsIt provides code quality monitoring for large enterprise projects, supports customized analysis rules, and meets enterprise-level development needs.