Industrial applications heavily rely on open-source software (OSS) libraries, which provide various benefits. But, they can also present a substantial risk if a vulnerability or attack arises and the community fails to promptly address the issue and release a fix due to inactivity. To be able to monitor the activities of such communities, a comprehensive list of repositories for the libraries of an ecosystem must be accessible. Based on these repositories, integrated libraries of an application can be monitored to observe whether they are adequately maintained. In this descriptive study, we analyze the accessibility of GitHub repositories for PyPI and NPM libraries. For all available libraries, we extract assigned repository URLs, direct dependencies and use the page rank algorithm to comprehensively analyze the ecosystems from a library and dependency chain perspective. For invalid repository URLs, we derive potential reasons. Both ecosystems show varying accessibility to GitHub repository URLs, depending on the page rank score of the analyzed libraries. For individual libraries, up to 73.8% of PyPI and up to 69.4% of NPM libraries have repository URLs. Within dependency chains, up to 80.1% of PyPI libraries have URLs, while up to 81.1% for NPM. That means, most libraries, especially the ones of increasing importance, can be monitored on GitHub. Among the most common reasons for invalid repository URLs is no URLs being assigned at all, which amounts up to 17.9% for PyPI and up to 39.6% for NPM. Package maintainers should address this issue and update the repository information to enable monitoring of their libraries.

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.