IMPACT OF BROKEN LINKS AND DEAD CODES ON OPEN-SOURCE REPOSITORIES: AN AI AUTO ENCODER APPROACH FOR SENSITIVE DATA PROTECTION

This article examines the effects of broken links and obsolete code on open-source repositories' usefulness, security, and sustainability. Defunct links, sometimes resulting from outdated or deleted external resources, hinder developers' access to essential documentation, libraries, and tools. Dead code, denoting abandoned or old code inside repositories, complicates maintenance and exposes possible risks. Furthermore, the proliferation of big data introduces distinct issues in managing large volumes of unstructured sensitive information, especially regarding extraction and analysis. This paper suggests implementing a Deep Autoencoder (DAE) model for protecting sensitive data. This method utilizes AI-driven auto encoders to identify sensitive data patterns, encrypt them, and discover weak connections and obsolete code for effective elimination. The optimized DAE algorithm demonstrates enhanced performance with increased detection rates, diminished false positives, and minimized failure jitter, making it a reliable option for risk assessment and prolonging the lifespan of open-source repositories. The results underscore the need for consistent maintenance and community cooperation to enhance open-source software ecosystems' quality, dependability, and security.