Integrated LLM-based Intelligent Comment Quality Analysis (CQI)

[ojaswishivam]

Overview

This PR introduces Comment Quality Intelligence (CQI)—a new feature that leverages Large Language Models (LLMs) to perform semantic audits of code documentation. Unlike traditional metrics that only count comment lines, CQI evaluates the meaning and relevance of comments in relation to the code they describe.

Key Features

• AI-Powered Semantic Auditing: Uses the Groq API (Llama-3.3-70B) to classify comments into categories: Warning, Intent, Amplification, Redundant, or Code-in-Comment.
• New Intelligence Metrics: Adds CQI score (0.0-5.0), CQI_Category (EXCELLENT, GOOD, etc.), and quantitative validation counts to methodMetrics.csv.
• Batch Processing: Implemented a batch analysis pipeline to optimize API performance.
• CLI Support: Added a new -llm flag to trigger the AI analysis.

Security & Documentation

• Security: Authentication is handled via the GROQ_API_KEY environment variable.
• README/CHANGELOG: Updated documentation to include the new features and execution instructions.

I have verified the build locally and it is ready for review. This feature adds a modern, intelligent layer to DesigniteJava's metrics suite.