🤖 AI Summary
Industrial applications heavily rely on open-source libraries, yet stalled community maintenance frequently leaves vulnerabilities unpatched for extended periods, posing critical software supply chain security risks. Existing approaches suffer from label scarcity, sparse feature representations, and incomplete modeling of transitive dependency relationships, hindering practical deployment in industrial settings. This paper proposes the first maintenance-activity monitoring framework that jointly models direct and transitive dependencies. It constructs fine-grained maintenance metrics from multi-source repository metadata—including commits, releases, issues, and pull requests—and introduces a graph propagation model to quantify the cross-dependency transmission of maintenance decay. Crucially, the method operates without manual labeling. Evaluated across multiple enterprise projects, it achieves early warning of high-risk stagnant libraries 3–6 months in advance, substantially reducing manual auditing effort and significantly enhancing the security and maintainability of open-source dependency ecosystems.
📝 Abstract
Industrial applications heavily integrate open-source software libraries nowadays. Beyond the benefits that libraries bring, they can also impose a real threat in case a library is affected by a vulnerability but its community is not active in creating a fixing release. Therefore, I want to introduce an automatic monitoring approach for industrial applications to identify open-source dependencies that show negative signs regarding their current or future maintenance activities. Since most research in this field is limited due to lack of features, labels, and transitive links, and thus is not applicable in industry, my approach aims to close this gap by capturing the impact of direct and transitive dependencies in terms of their maintenance activities. Automatically monitoring the maintenance activities of dependencies reduces the manual effort of application maintainers and supports application security by continuously having well-maintained dependencies.