BugHawk: A Unified AI Framework for Predictive Bug Analysis, Security Auditing and Automated Documentation

BugHawk is an AI-driven framework designed to perform predictive bug analysis, security auditing, and automated documentation generation for software projects. The system integrates Abstract Syntax Tree (AST) based structural validation with transformer-based semantic reasoning to detect vulnerabilities, logical errors, and potential security threats in source code. BugHawk also incorporates predictive defect hotspot modeling using version control history and provides a unified Project Health Score that aggregates security risks, bug density, performance bottlenecks, and code quality indicators. The framework is designed for fully offline deployment using quantized transformer models, enabling privacy-preserving AI-assisted debugging without reliance on external cloud services. Experimental evaluation demonstrates strong performance with an F1-score of 0.90 while maintaining low inference latency suitable for real-time development workflows. This work contributes to the development of hybrid structural–semantic AI architectures for intelligent software engineering and DevSecOps automation.