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Pyscn - AI Code Quality Analysis Tool, Quickly Discover and Clean Up Code Issues

Author: AI小集 · Original source: AI工具集

What is Pyscn

Pyscn is an intelligent code quality analysis tool designed specifically for Python developers, helping developers quickly discover and clean up issues in code, such as dead code, duplicate code, complex code structures, and more. It detects dead code through control flow graphs (CFG), identifies code clones using tree edit distance techniques, and analyzes code coupling and cyclomatic complexity. Pyscn's analysis speed is fast, reaching 100,000+ lines per second, and it supports multiple installation methods, including pipx and Go installation. Pyscn provides comprehensive analysis capabilities, supports generating HTML and JSON reports, and users can customize analysis rules through configuration files. It supports CI/CD integration, making it convenient to use in continuous integration environments.

Pyscn

Pyscn's Main Features

  • Dead Code Detection: Through control flow graph (CFG) analysis, quickly find unreachable dead code in the code, helping to clean up redundant code.
  • Code Clone Detection: Using tree edit distance (APTED) and locality-sensitive hashing (LSH) techniques, identify duplicate or similar code fragments and suggest refactoring opportunities.
  • Coupling Metrics: Calculate coupling between classes (CBO), helping developers monitor code architecture and optimize module dependencies.
  • Cyclomatic Complexity Analysis: Analyze the cyclomatic complexity of functions, identify functions with excessively high complexity, and suggest splitting them to improve code readability and maintainability.
  • Fast Analysis and Reporting: Support generating analysis reports in HTML and JSON formats, visually displaying code quality issues.
  • Flexible Configuration: Provide configuration files, allowing users to customize analysis rules to meet different project needs.
  • CI/CD Integration: Support 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) Analysis: By constructing a control flow graph of the code, detect unreachable code and identify dead code.
  • Tree Edit Distance (APTED) and Locality-Sensitive Hashing (LSH): Combine these two techniques to detect the similarity of code fragments and identify duplicate code.
  • Coupling Metrics (CBO): Calculate coupling between classes, helping to analyze the degree of modularization of the code.
  • Cyclomatic Complexity Calculation: Analyze the complexity of logical branches in the code and evaluate the complexity of functions.
  • High-Performance Implementation: Based on the Go language and tree-sitter technology, achieve fast code parsing and analysis, supporting efficient processing of large amounts of code.

Pyscn's Project Address

  • Github Repository：https://github.com/ludo-technologies/pyscn

Pyscn's Application Scenarios

  • Rapid Development Phase: In the process of pursuing rapid iteration and efficient development, help developers promptly discover and clean up issues in code and maintain code quality.
  • Code Maintenance and Refactoring: Identify duplicate code, dead code, and code with excessively high complexity, provide a basis for code refactoring, and optimize code structure.
  • Team Collaborative Development: In projects with multi-person collaboration, unify code quality standards and ensure that code submitted by team members meets quality requirements.
  • Teaching and Learning: As a teaching tool, it helps students and novice developers understand code quality issues and improve their coding skills.
  • Enterprise-level projects: Provides code quality monitoring for large enterprise projects, supports customized analysis rules, and meets enterprise-level development needs.