Identification-Method Research for Open-Source Software Ecosystems

Abstract

In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework.

Gradient-based Learning Methods Extended to Smooth Manifolds Applied to Automated Clustering

“…The goal of spectral/subspace clustering, which is a simple extension of traditional clustering, is to cluster data points that lie in a union of low-dimensional subspaces. Spectral clustering found a number of applications, as they are parts of more complex algorithms, like community detection algorithms (Makris, Pispirigos, & Rizos, 2020), film recommender systems for users (Cintia Ganesha Putri, Leu, & Seda, 2020), and algorithms to detect open-source software ecosystems (Liao, Wang, Liu, Zhang, Liu, & Zhang, 2019).…”