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Building a MuleSoft Package Validator with GitHub Copilot CLI

#devchallenge #githubchallenge #cli #githubcopilot

GitHub Copilot CLI Challenge Submission

This is a submission for the GitHub Copilot CLI Challenge

What I Built

MuleSoft Package Validator - An automated quality assurance and security validation tool for MuleSoft integration projects.

As a MuleSoft developer, I faced a recurring problem: manual code reviews were time-consuming (2+ hours per project) and inconsistent. Security vulnerabilities like hardcoded credentials would slip through to production, and orphaned flows bloated our applications. I built this CLI tool to solve these real-world challenges.

The Problem It Solves

  • 🔐 Security risks: Detects hardcoded passwords, API keys, JWT tokens across YAML, XML, and POM files
  • 📊 Code quality: Enforces naming conventions, flow complexity limits, and best practices
  • 🔍 Dead code: Identifies orphaned flows, unused configurations, and unreferenced components
  • 📦 Dependencies: Validates Maven dependencies and build sizes
  • ⚡ Time savings: Reduces validation from 2+ hours to under 2 minutes

Key Features

  • Multi-layer security scanning with context-aware detection
  • Flow and component complexity analysis
  • Orphan detection (unused flows, configs, properties)
  • Dependency management and build size validation
  • Beautiful HTML reports with actionable insights
  • Batch processing for multiple projects
  • 171 comprehensive tests (85% coverage)

Demo

🎬 Quick Start Demo 

# Clone and validate in under 60 seconds
git clone https://github.com/venkat-training/mulesoft_package_validator.git
cd mulesoft_package_validator
pip install -r requirements.txt
pip install -e .

# Test on included sample project
python -m mule_validator_cli --project ./samples/sample-mule-project
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📊 Sample Output 

================================================================================
VALIDATION REPORT
================================================================================

--- SECURITY WARNINGS ---
  ⚠️  YAML Secret detected in config-prod.yaml
      Location: database.password
      Value: "hardcoded_password_123"

  ⚠️  API key detected in config-dev.yaml
      Location: api.key

--- FLOW VALIDATION ---
  ✅ Flows: 8 (limit: 100)
  ⚠️  Invalid flow names: bad-flow, hello-worldFlow

--- ORPHAN DETECTION ---
  ⚠️  6 orphaned flows found
  ⚠️  16 unused property keys

TOTAL WARNINGS: 22
Report generated: validation_report.html
================================================================================
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🔗 Links

📸 Screenshots

HTML Validation Report

Validation Report

Orphan Detection Report

Orphan Report

My Experience with GitHub Copilot CLI

GitHub Copilot CLI transformed my development experience from day one. Here's how:

1. Test Generation (Saved ~15 hours)

The most impactful use was generating comprehensive test fixtures. Instead of manually crafting XML structures for 171 tests:

gh copilot suggest "generate pytest fixtures for XML parsing with multiple mule config files including flows, sub-flows, error handlers, and loggers"
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Copilot generated:

@pytest.fixture
def sample_mule_config():
    return """<?xml version="1.0" encoding="UTF-8"?>
    <mule xmlns="http://www.mulesoft.org/schema/mule/core">
        <flow name="testFlow">
            <logger message="test"/>
        </flow>
    </mule>"""

@pytest.fixture
def complex_flow_config():
    # Multiple flows with various components
    # Error handlers, sub-flows, etc.
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Impact: What would have taken me 2-3 hours of tedious XML writing was done in 10 minutes. I then focused on test logic rather than boilerplate.

2. Security Pattern Detection (Saved ~5 hours)

Building the security scanner required complex regex patterns:

gh copilot suggest "python regex patterns to detect JWT tokens, API keys, base64 encoded secrets, and AWS credentials in YAML files"
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Copilot provided:

JWT_PATTERN = r'eyJ[A-Za-z0-9_-]+\.eyJ[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+'
API_KEY_PATTERN = r'[a-zA-Z0-9]{32,}|sk_[a-z]+_[A-Za-z0-9]{32,}'
AWS_KEY_PATTERN = r'AKIA[0-9A-Z]{16}'
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These patterns became the core of my security scanner. I refined them, but the foundation saved hours of regex research and testing.

3. Error Handling Patterns

When dealing with malformed XML files:

gh copilot explain "How should I handle XML parsing errors in lxml when config files might be malformed"
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Copilot suggested:

try:
    tree = etree.parse(xml_file)
except etree.XMLSyntaxError as e:
    return {"error": f"XML syntax error: {str(e)}"}
except Exception as e:
    return {"error": f"Unexpected error: {str(e)}"}
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This pattern became my standard error handling template throughout the project.

4. Documentation Generation (Saved ~8 hours) 

gh copilot suggest "Generate comprehensive README sections for a Python CLI tool that validates MuleSoft packages including installation, usage examples, and troubleshooting"
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Copilot created the initial README structure that I refined. It included sections I hadn't considered (like Windows PATH configuration issues), which ended up being critical for users.

5. Library-Specific Syntax

When working with lxml XPath queries:

gh copilot suggest "lxml xpath to find all flow elements with name attribute in mule namespace"
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flows = root.xpath('.//mule:flow[@name]', namespaces=NAMESPACES)
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This saved me countless trips to documentation and StackOverflow.

💡 Key Takeaways

Where Copilot CLI Excelled:

  • ✅ Boilerplate generation: Test fixtures, mock data, standard patterns
  • ✅ Pattern suggestions: Regex, XPath, error handling templates
  • ✅ Library syntax: Quick answers for lxml, PyYAML, pytest
  • ✅ Documentation structure: Comprehensive README outline
  • ✅ Edge case handling: Suggested scenarios I hadn't considered

Where Human Judgment Was Essential:

  • 🧠 Architecture decisions: Module organization, validation flow
  • 🧠 Domain expertise: MuleSoft-specific validation rules
  • 🧠 User experience: CLI argument design, report formatting
  • 🧠 Testing strategy: What scenarios to test, assertion design

📊 Productivity Metrics

  • Total Development Time: 40 hours
  • Time Saved by Copilot CLI: ~15 hours (38% reduction)
  • Tests Written: 171 (85% with Copilot assistance)
  • Lines of Code: 3,500+
  • Documentation Pages: Wrote 80% faster with Copilot

Technical Highlights

Architecture 

CLI Entry Point
    ├── Flow Validator (naming, complexity)
    ├── Security Scanner (YAML, POM, XML)
    ├── Orphan Detector (unused components)
    ├── Dependency Analyzer (Maven validation)
    ├── Config Validator (YAML syntax)
    └── Report Generators (HTML, Console)
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Technology Stack

  • Python 3.8+ with lxml, PyYAML
  • 171 pytest tests (85% coverage)
  • HTML/CSS for beautiful reports
  • Maven integration for dependency checks

Real-World Impact

After deploying this tool in my team:

  • ✅ Zero hardcoded secrets reached production (down from ~2 per month)
  • ✅ Build sizes reduced 15% on average (detected unused dependencies)
  • ✅ 40% fewer orphaned flows in new projects
  • ✅ Code review time cut from 2 hours to 15 minutes

Try It Yourself!

Quick Start 

git clone https://github.com/venkat-training/mulesoft_package_validator.git
cd mulesoft_package_validator
pip install -r requirements.txt
pip install -e .

# Run on sample project
python -m mule_validator_cli --project ./samples/sample-mule-project --output report.html
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What You'll See

The sample project includes intentional issues:

  • Hardcoded passwords and API keys
  • Invalid flow names (bad-flow, INVALIDFLOW123)
  • 6 orphaned flows
  • Excessive logging
  • Unused dependencies

Perfect for testing the validator's capabilities!

Full Documentation

Check out the comprehensive README with:

  • Installation guide (including Windows PATH setup)
  • Usage examples (CLI and Python API)
  • Sample reports and test project
  • Troubleshooting guide
  • Contributing guidelines

Lessons Learned

1. Start with Copilot CLI for Patterns, Not Architecture

I initially tried using Copilot to design my entire module structure. It gave generic advice. Instead, I found it excelled at generating specific patterns once I knew what I needed:

  • "Generate regex for JWT tokens" ✅
  • "Design validation architecture" ❌

2. Test Fixtures Are Perfect for AI

The most productivity gain came from test generation. Tests follow predictable patterns, making them ideal for Copilot:

  • Fixture structure is repetitive
  • Edge cases follow templates
  • Mock data needs variety but patterns

3. Iterate on Copilot's Output

Copilot CLI rarely gives production-ready code on the first try. But it gives you a 95% solution that you can refine. This is still far faster than starting from scratch.

4. Combine Copilot with Domain Expertise

The best results came when I combined:

  • Copilot's knowledge: Python patterns, library syntax, common practices
  • My expertise: MuleSoft-specific rules, security requirements, user needs

What's Next?

Future enhancements I'm planning:

  • GitHub Actions integration for CI/CD pipelines
  • VS Code extension for real-time validation
  • Custom rule engine for team-specific standards
  • Real-time validation with file watchers

Acknowledgments

  • Built with ❤️ for the MuleSoft developer community
  • Powered by GitHub Copilot CLI (saved 15+ hours of development time!)
  • Inspired by real-world integration challenges at scale

⭐ If you find this useful, please star the repo!

GitHub: https://github.com/venkat-training/mulesoft_package_validator

Thanks for reading! Questions or feedback? Drop a comment below or open an issue on GitHub.

Top comments (1)

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venkattraining profile image
venkat-training • • Edited

Good to see 267 downloads in pepy.tech portal. for this package. Thanks folks.