Code Analysis Options
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:
-
Select analysis type based on need
- Pick the right analysis for your goal
-
Run appropriate tools and inspections
- Execute the relevant tools and checks
-
Generate comprehensive report
- Produce a thorough report
-
Provide actionable recommendations
- Give specific, actionable suggestions
-
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
| Tool | Language | Purpose |
|---|---|---|
| ESLint | JavaScript/TypeScript | Code style checking |
| SonarQube | Multi-language | Comprehensive quality |
| CodeClimate | Multi-language | Maintainability |
| Snyk | Multi-language | Security scanning |
Performance Tools
| Tool | Purpose |
|---|---|
| Lighthouse | Web performance |
| Chrome DevTools | Runtime analysis |
| Webpack Bundle Analyzer | Bundle analysis |
| clinic.js | Node.js performance |
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.
📚 相关资源
❓ 常见问题
关于本章主题最常被搜索的问题，点击展开答案
本章列了几种代码分析类型？
六种：(1) Knowledge Graph Generation — 组件关系 / 依赖可视化 / 架构模式识别；(2) Code Quality — 圈复杂度 / 可维护性指数 / 技术债 / 代码重复；(3) Performance — 瓶颈定位 / 内存模式 / 算法复杂度 / DB 查询；(4) Security — 漏洞扫描 / 输入校验 / 认证授权 / 敏感数据；(5) Architecture — SOLID / 耦合内聚 / 模块边界；(6) Test Coverage — 覆盖率 / 未测路径 / 测试质量。
Static analysis 工具该选 ESLint / SonarQube / CodeClimate / Snyk 里哪个？
用途不同各有位置：ESLint 专 JS/TS 代码规范，PR 必跑；SonarQube 多语言综合质量，含 bug / smell / security 报告，适合 CI 主门禁；CodeClimate 重维护性指数和趋势图，monorepo 长期跟踪；Snyk 专漏洞扫描（依赖 CVE + 代码层），跟 GitHub Dependabot 互补。理想组合 = ESLint + SonarQube + Snyk。
Web 性能分析用 Lighthouse 还是 Chrome DevTools？
Lighthouse 出综合评分（Performance / Accessibility / SEO / Best Practices）和 LCP / CLS / TBT 等核心指标，适合 PR 自动化跑、定期回归；Chrome DevTools 走运行时分析（Timeline / Heap / Network），定位单个慢操作时更精准。先用 Lighthouse 圈到「哪一页有问题」，再用 DevTools 钻进去看「具体哪个调用」。
代码分析报告应该怎么组织才能让人看完就动手？
本章 5 节模板：Executive Summary（关键发现 + 整体评估）、Detailed Findings（按类别分组，每条带 Location 文件:行号 + Severity High/Medium/Low + Recommendation）、Risk Assessment（潜在影响）、Improvement Roadmap（Immediate 本周 / Short-term 本月 / Long-term 本季）、Code Examples。没有 Location 和 Severity 的 finding 全是空话。
让 AI 做代码分析的 prompt 怎么写效果好？
本章给的模板四件套：「请对以下代码进行分析：1. 代码质量评估；2. 潜在性能问题；3. 安全风险；4. 改进建议」。关键是显式列维度而不是只问"帮我看下这段代码"。建议加上语言 / 框架版本 / 业务场景三件上下文，AI 才不会给出泛泛的「考虑使用更现代的语法」这种废话。