BugHawk is an AI-driven framework designed to improve software quality through predictive bug analysis, automated security auditing, and intelligent documentation generation.
Modern software systems rely on rapid development cycles, continuous integration pipelines, and AI-assisted coding tools. While these technologies increase productivity, they also introduce new security vulnerabilities, logical errors, and maintenance challenges. Traditional static analysis tools detect only known patterns and often fail to capture contextual logic flaws.
BugHawk introduces a hybrid structural–semantic architecture that combines Abstract Syntax Tree (AST) based structural validation with transformer-based semantic reasoning. This approach enables the system to detect security vulnerabilities, logic-level defects, and risky coding patterns more effectively than standalone static or machine learning tools.
The framework also incorporates predictive defect hotspot analysis using version control metadata and generates a unified Project Health Score that evaluates software quality across security, bug density, performance risks, and maintainability indicators.
Designed as a privacy-preserving offline AI system, BugHawk operates without reliance on cloud-based inference services. Through quantized transformer models and efficient architecture design, the framework enables real-time vulnerability detection and debugging support on consumer hardware.
This book presents the complete design and implementation of the BugHawk framework, including its architecture, AI model design, experimental evaluation, deployment strategy, and future research directions.
Academic DOI: https://doi.org/10.5281/zenodo.18864292
This monograph is archived on Zenodo for academic citation and long-term preservation.
