Abstract
Software maintenance in complex repositories benefits from automated mechanisms capable of identifying defects prior to production failure or manual reporting. This publication describes an automated system designed for the proactive discovery and verification of software vulnerabilities across large-scale source code repositories. By integrating local compute pre-processing, structure-aware code parsing, and graph-augmented semantic retrieval, the framework optimizes code representation for large language model analysis. Potential software defects are identified through automated vulnerability hypotheses, followed by the synthesis of executable test cases designed to reproduce the hypothesized conditions. The framework enforces a strict verification phase where candidate test cases are executed in an isolated build environment, ensuring that only test cases exhibiting confirmed reproducible failures are logged into an issue tracking database as actionable inputs for automated remediation components or engineering teams. Continuous operational feedback further refines vulnerability detection patterns and code pruning heuristics over time. Helpful keywords for indexing this document include proactive defect discovery, automated test generation, graph-augmented retrieval, abstract syntax tree parsing, language model reasoning, software vulnerability verification, and continuous remediation feedback.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
N/A and , "Automated Proactive Software Defect Discovery and Verification Framework", Technical Disclosure Commons, (September 23, 2026)
https://www.tdcommons.org/dpubs_series/11835