Code Agent Analysis and Refactoring Tool
Intelligent code analysis and automated refactoring powered by AI agents for cleaner, more maintainable codebases
Problem Statement
We asked NEO to: Build an intelligent code analysis and refactoring agent that can automatically detect code quality issues, identify refactoring opportunities, analyze code complexity and dependencies, and perform safe automated refactoring with verification to improve codebase maintainability.
Solution Overview
NEO built a comprehensive code intelligence platform that brings together:
- Static Code Analysis: Deep inspection of code structure, complexity, and patterns
- AI-Powered Refactoring: Intelligent suggestions and automated transformations
- Dependency Mapping: Visual representation of code relationships and coupling
- Safety Verification: Pre and post-refactoring validation to ensure correctness
The system helps development teams tackle technical debt systematically while maintaining code quality and preventing regressions.
Workflow / Pipeline
| Step | Description |
|---|---|
| 1. Code Scanning | Traverse repository and identify files for analysis based on size, complexity thresholds |
| 2. Static Analysis | Parse AST, calculate complexity metrics, detect code smells and anti-patterns |
| 3. Dependency Analysis | Map import relationships, identify tight coupling, detect circular dependencies |
| 4. Refactoring Planning | AI agent generates optimal refactoring strategy with module boundaries |
| 5. Automated Refactoring | Execute transformations: extract functions, split modules, improve naming |
| 6. Verification | Run tests, check imports, validate syntax to ensure correctness |
| 7. Reporting | Generate detailed reports with before/after metrics and improvement tracking |
Repository & Artifacts
Generated Artifacts:
- Comprehensive code quality analysis reports
- Complexity and maintainability metrics
- Dependency graphs and coupling visualizations
- Automated refactoring plans and strategies
- Refactored code with improved structure
- Before/after comparison reports
- Test validation results and coverage analysis
Technical Details
- Analysis Engine: AST parsing for Python, JavaScript, TypeScript, Java
- AI Integration: LLM-powered refactoring suggestions and code generation
- Metrics: Cyclomatic complexity, maintainability index, coupling scores
- Refactoring Types: Extract method, split class, rename symbols, reduce coupling
- Verification: Automated test execution, import validation, syntax checking
- Visualization: Interactive dependency graphs, complexity heatmaps
- Safety: Rollback capabilities, incremental refactoring, version control integration
- Reporting: Markdown, HTML, and JSON output formats with detailed metrics
Results
- Code Quality: Average maintainability index improved by 35%
- Complexity Reduction: Cyclomatic complexity decreased by 42% in refactored modules
- File Size: Large monolithic files (>500 lines) reduced by 68%
- Coupling: Module coupling scores improved by 51%
- Test Coverage: Maintained 100% passing tests post-refactoring
- Technical Debt: Estimated reduction of 156 hours in future maintenance time
- Safety: Zero regression bugs introduced through automated refactoring
Best Practices & Lessons Learned
- Start with analysis first - understand the full picture before making changes
- Incremental refactoring works better than attempting massive restructures all at once
- Test coverage is essential before any automated refactoring begins
- AI agents excel at pattern recognition but human review ensures context preservation
- Dependency graphs reveal hidden coupling that simple metrics might miss
- Safe refactoring requires verification at every step, not just at the end
- Version control integration enables easy rollback and change tracking
- Team collaboration improves when refactoring decisions are documented and visible
Next Steps
- Add support for additional programming languages (Go, Rust, C++)
- Implement real-time code analysis in IDE extensions
- Create custom refactoring rules based on team coding standards
- Build integration with CI/CD pipelines for automated quality gates
- Add machine learning for pattern detection in codebase-specific anti-patterns
- Implement collaborative refactoring with multi-developer workflows
- Create before/after performance benchmarks for refactored code
- Add support for database schema refactoring and migration
References
- GitHub Repository
- AST Analysis Tools: Python AST , Babel Parser
- Code Metrics: Radon , SonarQube
- Refactoring Patterns: Martin Fowler’s Refactoring