138 Aspen
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Scala Rust Mathematica PythonMatlab
| Aspect | What to Check | Why It Matters |
|---|---|---|
| Code Quality | - Code readability & clarity - Consistent naming conventions - Proper indentation & formatting - Comments & documentation - Code duplication |
Makes code maintainable and easier for other developers to understand |
| Functionality | - Code does what it's supposed to do - Edge cases handled - Input validation - Error handling - Business logic correctness |
Ensures the code meets requirements and works reliably |
| Performance | - Algorithm efficiency - Resource usage - Database query optimization - Memory management - Loop efficiency |
Affects application speed, resource consumption, and scalability |
| Security | - Input sanitization - Authentication/Authorization - Data encryption - Protection against common vulnerabilities - Secure data handling |
Protects against security breaches and data leaks |
| Testing | - Unit tests coverage - Integration tests - Edge case testing - Test quality - Mocking strategy |
Ensures code reliability and makes future changes safer |
| Architecture | - Design patterns usage - Code organization - Dependencies management - Separation of concerns - Module coupling |
Affects long-term maintainability and scalability |
| Best Practices | - Language-specific conventions - Framework guidelines - Company standards - Design principles (SOLID, DRY, etc.) - Clean code principles |
Maintains consistency and quality across the codebase |
| Version Control | - Commit message clarity - Branch strategy - Merge conflicts - Code history - Change scope |
Facilitates collaboration and code management |
| Dependencies | - Library versions - License compliance - Security vulnerabilities - Dependency conflicts - Size and impact |
Affects security, legal compliance, and maintenance |
| Documentation | - API documentation - README updates - Release notes - Configuration guides - Deployment instructions |
Enables effective use and maintenance of the code |
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