Open Source Licensing: Software Freedom and Intellectual Property Law
Open Source Licensing: Software Freedom and Intellectual Property Law
Knowledge Reuse in Open Source Software: An Exploratory Study of 15 Open Source Projects
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 07
Mining large software compilations over time: another perspective of software evolution
Proceedings of the 2006 international workshop on Mining software repositories
Macro-level software evolution: a case study of a large software compilation
Empirical Software Engineering
Journal of Software Maintenance and Evolution: Research and Practice
The beauty and the beast: vulnerabilities in red hat’s packages
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Communicating continuous integration servers for increasing effectiveness of automated testing
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
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Success in the open source software world has been measured in terms of metrics such as number of downloads, number of commits, number of lines of code, number of participants, etc. These metrics tend to discriminate towards applications that are small and tend to evolve slowly. A problem is, however, how to identify applications in these latter categories that are important. Software distributions specify the dependencies needed to build and to run a given software application. We use this information to create a dependency graph of the applications contained in such a distribution. We explore the characteristics of this graph, and use it to define some metrics to quantify the dependencies (and dependents) of a given software application. We demonstrate that some applications that are invisible to the final user (such as libraries) are widely used by end-user applications. This graph can be used as a proxy to measure success of small, slowly evolving free and open source software.