Feature Enhancement: AI-Powered Project Summary and Labeling

[DonnieBLT]

Objective:

Enhance the existing project section in the OWASP BLT project to automatically collect, analyze, and summarize information from project repositories using AI. This feature will generate a concise summary of each project, along with searchable labels to improve discoverability and usability.

Detailed Description:

1. Repository Data Collection:

   • Implement a system to regularly fetch and update data from project repositories. This data can include README files, documentation, commit messages, and issue trackers.
   • Utilize APIs (such as GitHub, GitLab, etc.) to extract relevant information automatically.

2. AI-Powered Project Summarization:

   • Integrate an AI model capable of natural language processing (NLP) to analyze the collected repository data.
   • The AI should generate a brief, human-readable summary of each project, focusing on key aspects such as purpose, features, technologies used, and current status.
   • Summaries should be automatically updated whenever significant changes occur in the repository.

3. Label Generation:

   • Develop an AI-based labeling system that categorizes projects with relevant tags or labels.
   • Labels could include technology stacks (e.g., Python, JavaScript), project type (e.g., web application, library, tool), OWASP relevance (e.g., security testing, secure coding), and more.
   • These labels should be dynamically updated and allow users to search or filter projects easily.

4. Searchable Project Catalog:

   • Enhance the project's section UI/UX to include a search and filter functionality based on the generated summaries and labels.
   • Provide users with the ability to quickly find projects relevant to their interests by searching for specific labels or keywords found in the AI-generated summaries.

5. Contributors and Links Integration:

   • Maintain the current functionality that displays contributors and repository links.
   • Integrate the new summaries and labels alongside this existing information to provide a comprehensive overview of each project.

6. Admin and User Feedback Loop:

   • Include a feedback mechanism for project maintainers and users to suggest edits to the AI-generated summaries or labels.
   • Allow maintainers to manually adjust summaries and labels if necessary, ensuring the AI output aligns with project goals and descriptions.

Technical Implementation:

• Backend:

  • Use Python-based NLP libraries (e.g., spaCy, Hugging Face Transformers) for AI summarization and labeling.
  • Implement data collection using repository APIs and schedule regular updates via cron jobs or similar task schedulers.

• Frontend:

  • Update the UI to display the AI-generated summaries and labels alongside existing project details.
  • Enhance the search functionality to utilize the generated labels and summary content.

• Data Storage:

  • Store AI-generated summaries and labels in a database, ensuring they can be efficiently queried and updated.

• Performance Considerations:

  • Ensure the AI summarization and labeling processes are optimized for speed, so they do not slow down the user experience.
  • Consider using caching strategies for frequently accessed summaries and labels.

Benefits:

• Improved Discoverability:

  • Users can quickly find projects that match their interests, improving engagement and collaboration.

• Up-to-Date Information:

  • Summaries are kept current with the latest project developments, providing accurate insights.

• Enhanced User Experience:

  • Streamlined access to project information, making the OWASP BLT project section more user-friendly and valuable.

Future Enhancements:

• Expand the AI model to provide insights or predictions about the project’s future trajectory based on the repository activity.
• Introduce advanced filtering options like popularity, recent updates, or specific contributor involvement.
• Implement multilingual support for summarization and labeling to reach a global audience.