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2.2 Specialized Tools in the SDLC

flowchart LR
    subgraph Plan["📋 Planning"]
        A1[Requirements] --> A2[AI generates
user stories]
    end

    subgraph Code["💻 Development"]
        B1[Write Code] --> B2[Copilot assists]
        B2 --> B3[AI reviews PR]
    end

    subgraph Test["🧪 Testing"]
        C1[Generate tests] --> C2[AI finds bugs]
        C2 --> C3[Visual testing]
    end

    subgraph Deploy["🚀 Deployment"]
        D1[Security scan] --> D2[Performance check]
        D2 --> D3[Auto-docs]
    end

    subgraph Monitor["📊 Monitor"]
        E1[Log analysis] --> E2[AI detects
anomalies]
    end

    Plan --> Code --> Test --> Deploy --> Monitor
    Monitor -.->|Feedback| Plan

    style Plan fill:#e1f5ff
    style Code fill:#fff4e1
    style Test fill:#ffe1f5
    style Deploy fill:#e1ffe1
    style Monitor fill:#f5e1ff

Key Points to Cover:

  • Code Analysis Tools
  • AI-powered static code analysis
  • Code quality metrics and recommendations
  • Technical debt identification

  • Examples: DeepCode, Codacy AI features, Amazon CodeGuru

  • Security-Focused AI Tools

  • Vulnerability detection and prevention
  • Security pattern recognition
  • Automated security code reviews
  • Examples: Snyk, GitHub Advanced Security, Checkmarx

  • SAST (Static Application Security Testing) enhancements

  • Automated Testing Tools

  • AI-driven test generation
  • Test case prioritization
  • Flaky test detection
  • Visual testing and UI validation

  • Examples: Testim, Applitools, Mabl

  • Code Review Automation

  • AI-assisted pull request reviews
  • Consistency checking
  • Best practice enforcement

  • Examples: CodeRabbit, Codium AI

  • Documentation Tools

  • Automatic documentation generation
  • API documentation from code
  • Keeping docs in sync with code

  • Examples: Mintlify, Swimm

  • Performance and Optimization

  • AI-driven performance profiling
  • Bottleneck identification

  • Optimization suggestions

  • Integration in CI/CD Pipelines

  • Incorporating AI tools into build processes
  • Automated quality gates

  • Continuous feedback loops

  • Tool Selection Criteria

  • Team size and needs
  • Language and framework support
  • Cost-benefit analysis
  • Privacy and data security considerations