Vol. 2 No. 6 (2025)

This issue explores automated software quality assurance through intelligent code analysis, featuring groundbreaking research on comment consistency detection algorithms. The study addresses critical challenges in software evolution where code modifications often leave comments outdated, creating technical debt and increasing maintenance costs. The research integrates code-comment association graphs with AST-based differential analysis and natural language processing techniques to systematically identify inconsistencies using version control history. Experimental validation across five major open-source projects demonstrates exceptional performance: 89.6% precision, 92.3% recall, and only 6.2% false positive rate, significantly outperforming existing baseline methods. This research showcases artificial intelligence's transformative potential in software engineering practices, providing practical solutions for technical debt management and automated code quality control in modern development environments.