Code Analysis Options

Code Analysis Overview

Code analysis is one of the most effective ways to improve code quality. Different analysis types catch different problems -- potential bugs, performance bottlenecks, security gaps.

Analysis Menu

1. Knowledge Graph Generation

Generate a code knowledge graph to visualize structure:

• Map relationships between components - Dependency relationships between modules
• Visualize dependencies - Dependency visualization
• Identify architectural patterns - Spot architecture patterns

Knowledge graphs help you understand the overall structure of complex codebases.

2. Code Quality Evaluation

Key metrics for evaluating code quality:

• Complexity metrics - Cyclomatic complexity, cognitive complexity
• Maintainability index - How maintainable the code is
• Technical debt assessment - How much tech debt has piled up
• Code duplication detection - Finding copy-pasted code

3. Performance Analysis

Find the bottlenecks:

• Identify bottlenecks - Locate performance hot spots
• Memory usage patterns - How memory is being used
• Algorithm complexity - Algorithm complexity analysis
• Database query optimization - Slow query optimization

4. Security Review

Spot vulnerabilities:

• Vulnerability scanning - Scan for known vulnerabilities
• Input validation checks - Are inputs being validated?
• Authentication/authorization review - Auth and permissions audit
• Sensitive data handling - How sensitive data is managed

5. Architecture Review

Verify design quality:

• Design pattern adherence - Are design patterns being followed?
• SOLID principles compliance - SOLID principles alignment
• Coupling and cohesion analysis - Coupling and cohesion metrics
• Module boundaries - Are module boundaries clear?

6. Test Coverage Analysis

Evaluate test coverage:

• Coverage percentages - Coverage stats
• Untested code paths - Code paths with no tests
• Test quality assessment - Quality of existing tests
• Missing edge cases - Edge cases that aren't covered

Process

Standard workflow for code analysis:

1. Select analysis type based on need

   • Pick the right analysis for your goal

2. Run appropriate tools and inspections

   • Execute the relevant tools and checks

3. Generate comprehensive report

   • Produce a thorough report

4. Provide actionable recommendations

   • Give specific, actionable suggestions

5. Prioritize improvements by impact

   • Rank improvements by how much they'll help

Output Format

Standard format for analysis reports:

## Executive Summary

Key findings and overall assessment

## Detailed Findings

### Category 1: [Issue Type]

-   Finding 1: [Description]
    -   Location: [File:Line]
    -   Severity: [High/Medium/Low]
    -   Recommendation: [Suggestion]

### Category 2: [Issue Type]

...

## Risk Assessment

Risk evaluation and potential impact

## Improvement Roadmap

1. Immediate (This Week): [Urgent fixes]
2. Short-term (This Month): [Important improvements]
3. Long-term (This Quarter): [Optimization projects]

## Code Examples

Relevant code examples and fix suggestions

Common Tools

Static Analysis Tools

Performance Tools

Using AI for Code Analysis

You can have AI help with code analysis:

Analyze the following code for:
1. Code quality assessment
2. Potential performance issues
3. Security risks
4. Improvement suggestions

[Paste code]

Next Steps

Check out GitHub Issue Analysis to learn how to turn issues into technical specs.