Code Quality Assessment - Charlie Cullen

Project Overview

This project provides an automated code quality assessment tool that analyzes Python codebases for style violations, complexity metrics, and best practice adherence. It generates comprehensive reports with actionable recommendations to improve code maintainability and readability.

Key Features

Static code analysis using AST parsing
Complexity metrics calculation (cyclomatic complexity, maintainability index)
PEP 8 style checking and enforcement
Code smell detection and reporting
Detailed HTML/JSON report generation
Integration with CI/CD pipelines

Technical Implementation

Architecture

The tool uses a modular architecture for analyzing different aspects of code quality:

Parser Module: AST-based code parsing and tree traversal
Analyzer Module: Metrics calculation and pattern detection
Reporter Module: Report generation in multiple formats
CLI Interface: Command-line tool for easy integration

Technologies Used

Python AST: Abstract Syntax Tree parsing
pylint/flake8: Style checking integration
radon: Complexity metrics calculation
Jinja2: HTML report templating
argparse: Command-line interface

Challenges & Solutions

Challenge: Handling Large Codebases

Solution: Implemented parallel processing and incremental analysis to handle projects with thousands of files efficiently.

Challenge: False Positive Reduction

Solution: Developed configurable rule sets and context-aware analysis to reduce noise in reports.

Challenge: Report Clarity

Solution: Created visual dashboards with severity levels and prioritized recommendations for better actionability.

What I Learned

Deep understanding of Python AST and metaprogramming
Software quality metrics and their practical applications
Design patterns for building extensible analysis tools
Best practices for code maintainability and readability
CLI tool development and user experience design

Project Links

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