Restructure, modernize project structure and CI workflows

[kyhau]

Added

• Community health files: CODE_OF_CONDUCT.md, CONTRIBUTING.md, SECURITY.md, CODEOWNERS
• Issue templates for bug reports and feature requests with structured forms
• .dockerignore for optimized Docker builds
• Snyk security scanning workflow
• Modern Makefile targets: setup-init, format-python, lint-python, pre-commit
• Black code formatter for consistent code style
• 15 new unit tests improving code coverage from 83% to 91%:
  • Exception handling tests for process() function
  • CLI argument parsing tests (parse_args with default and custom arguments)
  • Main entry point tests
  • API retry logic tests (429 and 529 error handling)
  • Error status handling tests
  • Endpoint filtering tests (skipping "Unable" endpoints)
  • Print message variant tests (DEBUG, WAIT_FOR_COMPLETE, etc.)
  • Actual requests_get() implementation tests (with and without email)

Changed

• Moved tests from ssllabsscan/tests/ to root-level tests/ directory (Python best practice)
• Renamed CI workflow from build-and-test.yml to ci.yml for consistency
• Updated all Makefile targets to match standard conventions across projects
• Migrated from standalone mock package to built-in unittest.mock
• Reorganized dependencies: moved flake8 from test to dev dependencies
• Cleaned up pyproject.toml: removed unused dependencies (mock, coverage, pytest-gitignore, setuptools, wheel)
• Improved Dockerfile with two-phase installation and cache cleanup
• Updated LICENSE copyright to 2017-2025 with author name
• Enhanced README with better structure, badges, and documentation
• Modernized all .github workflows with concurrency control
• Simplified .yaml-lint.yml configuration
• Updated dependabot.yml to remove redundant pip ecosystem
• Updated pull request template for better clarity

Removed

• Unused dependencies: mock, coverage, pytest-gitignore, setuptools, wheel
• Old ssllabsscan/tests/ directory (moved to root)
• Redundant application-specific patterns from .gitignore

[amazon-q-developer]

⏳ Code review in progress. Analyzing for code quality issues and best practices. Detailed findings will be posted upon completion.

Using Amazon Q Developer for GitHub

Amazon Q Developer¹ is an AI-powered assistant that integrates directly into your GitHub workflow, enhancing your development process with intelligent features for code development, review, and transformation.

Slash Commands

Command	Description
/q <message>	Chat with the agent to ask questions or request revisions
/q review	Requests an Amazon Q powered code review
/q help	Displays usage information

Features

Agentic Chat
Enables interactive conversation with Amazon Q to ask questions about the pull request or request specific revisions. Use /q <message> in comment threads or the review body to engage with the agent directly.

Code Review
Analyzes pull requests for code quality, potential issues, and security concerns. Provides feedback and suggested fixes. Automatically triggered on new or reopened PRs (can be disabled for AWS registered installations), or manually with /q review slash command in a comment.

Customization

You can create project-specific rules for Amazon Q Developer to follow:

1. Create a .amazonq/rules folder in your project root.
2. Add Markdown files in this folder to define rules (e.g., cdk-rules.md).
3. Write detailed prompts in these files, such as coding standards or best practices.
4. Amazon Q Developer will automatically use these rules when generating code or providing assistance.

Example rule:

All Amazon S3 buckets must have encryption enabled, enforce SSL, and block public access.
All Amazon DynamoDB Streams tables must have encryption enabled.
All Amazon SNS topics must have encryption enabled and enforce SSL.
All Amazon SNS queues must enforce SSL.

Feedback

To provide feedback on Amazon Q Developer, create an issue in the Amazon Q Developer public repository.

For more detailed information, visit the Amazon Q for GitHub documentation.

Footnotes

1. Amazon Q Developer uses generative AI. You may need to verify generated code before using it in your environment. See the AWS Responsible AI Policy. ↩

[kyhau]

✅ Snyk checks have passed. No issues have been found so far.

Status	Scanner	Critical	High	Medium	Low	Total (0)
✅	Open Source Security	0	0	0	0	0 issues

💻 Catch issues earlier using the plugins for VS Code, JetBrains IDEs, Visual Studio, and Eclipse.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 96.22%. Comparing base (958ee03) to head (9cf327f).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff             @@
##             main     #253       +/-   ##
===========================================
+ Coverage   83.43%   96.22%   +12.79%     
===========================================
  Files           4        4               
  Lines         163      159        -4     
===========================================
+ Hits          136      153       +17     
+ Misses         27        6       -21     

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[amazon-q-developer]

Excellent Modernization Effort! 🚀

This is a comprehensive and well-executed modernization of the project structure and CI workflows. The changes demonstrate strong engineering practices and attention to detail.

✅ Highlights

• Modern Python tooling: Great addition of Black formatter and improved Makefile targets
• Enhanced CI/CD: Improved workflows with concurrency control and better organization
• Community health: Excellent addition of CODE_OF_CONDUCT.md, CONTRIBUTING.md, SECURITY.md, and issue templates
• Dependency cleanup: Smart removal of redundant dependencies and proper Poetry configuration
• Test structure: Moving tests to root-level tests/ directory follows Python best practices
• Docker improvements: Better layer caching and two-phase installation approach

🔧 Key Issues to Address

1. Missing coverage dependency in pyproject.toml - this could cause test failures
2. Security vulnerability in Snyk workflow using @master instead of pinned version
3. Hardcoded Python path in Dockerfile that will break with version changes

📊 Impact Assessment

• Code coverage: Improved from 83% to 91% with 15 new unit tests
• Developer experience: Significantly enhanced with modern Makefile targets and clear documentation
• Security posture: Strengthened with multiple scanning tools and proper community health files
• Maintainability: Much improved with standardized tooling and clear contribution guidelines

The changes are well-structured and maintain backward compatibility while modernizing the development workflow. Once the identified issues are addressed, this will be an excellent foundation for future development.

Recommendation: Approve after addressing the critical issues mentioned in the review comments.