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

CI/CD pipelines, along with the proliferation in the use of AI-assisted code generation, have shifted how developers create software today; however, this shift has caused several new types of security challenges as well as fragmentation within every aspect of the software quality toolchain. The result: Many developers need to use disparate solutions each with a different level of sophistication, functionality, and effectiveness for vulnerability detection, bug analysis, and generating documentation creating additional overhead, which ultimately delays the time it takes to respond to security issues. This paper presents BugHawk, a cohesive AI platform that uses a hybrid analysis methodology combining deterministic AST parsing with an optimized transformer-based classifier capable of detecting the most severe vulnerabilities such as SQL Injection and Cross-Site Scripting during the predictive bug analysis, automated security auditing, and documentation generation phases of the software development lifecycle. The framework also integrates a predictive model for identifying future defect hotspots from version-control history and an NLP-based module for automated documentation generation. Experimental evaluation on a dataset of 68,728 code samples demonstrates that BugHawk achieves an F1-score of 0.90 in detecting security threats, while maintaining a low false-positive rate.