Identification-Method Research for Open-Source Software Ecosystems

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.

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The Impact of Project Initiators on Open Source Software Project Success

Research Anthology on Usage and Development of Open Source Software ◽

10.4018/978-1-7998-9158-1.ch012 ◽

2021 ◽

pp. 221-238

Author(s):

Ling Wang ◽

Jinxiao Wang

Keyword(s):

Open Source ◽

Open Source Software ◽

Project Success ◽

Software Project ◽

Software Projects ◽

Collective Behaviors ◽

Collective Wisdom ◽

Open Source Software Project ◽

Development Costs ◽

Collaborative Innovation

This paper focuses on studying the role of open source software project initiator in affecting the OSS project success from the perspective of individual and collective behaviors. The authors collected the data from an emerging OSS hosting platform Gitee in China. This research indicates that the success mode for open source software projects in China relies a lot on the project initiators. Project initiators not only contribute codes to aid the project directly, but also use their social capital to facilitate the project success. But no full play has been given to social network's effect on mass production and collaborative innovation. The authors suggest collaborative innovation which could lead to coherence of global collective wisdom, reduced development costs, and expanded source of innovation should be the further direction for the OSS project in emerging platforms.

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What Is Needed for the Sustainable Success of OSS Projects: Efficiency Analysis of Commit Production Process via Git

Sustainability ◽

10.3390/su10093001 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3001

Author(s):

Jaeyoon Song ◽

Changhee Kim

Keyword(s):

Open Source ◽

Open Source Software ◽

Coordination Mechanism ◽

Software Project ◽

Tobit Regression ◽

Software Projects ◽

Open Source Software Project ◽

Regulatory Processes ◽

Massive Number ◽

Tobit Regression Analysis

The purpose of this study is to investigate the relative efficiency of open source software projects, and to analyze what is needed for their sustainable success. The success of open source software is known to be attributable to a massive number of contributors engaging in the development process. However, an efficient open source software project is not guaranteed simply by active participation by many; a coordination mechanism is needed to seamlessly manage the multi-party collaboration. On this basis, this study aimed to examine the internal regulatory processes based on Git and GitHub, which serve as such a mechanism, and redefine the efficiency of open source software projects to fully reflect them. For this purpose, a two-stage data envelopment analysis was used to measure the project efficiency reflecting the internal processes. Moreover, this study considered the Kruskal–Wallis test and Tobit regression analysis to examine the effects of the participation by many on an open source software project based on the newly defined efficiency. Results show that a simple increase in contributors can be poisonous in terms of the efficiency of open source software projects.

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Assessing the Health of the Dark Web:

10.1007/978-3-030-88583-0_12 ◽

2021 ◽

pp. 125-134

Author(s):

Samuel Onyango ◽

Emilie Steenvoorden ◽

Joram Scholten ◽

Slinger Jansen

Keyword(s):

Open Source ◽

Open Source Software ◽

Ecosystem Health ◽

World Wide ◽

Software Projects ◽

Ecosystem Research ◽

Software Ecosystem ◽

Software Products ◽

The World ◽

Dark Web

AbstractA hidden part of the World Wide Web is known as the Dark Web, featuring websites that cannot be indexed by traditional search engines. Many open source software products are used to access and navigate through the Dark Web. Together they form the Dark Web open source software ecosystem. Research on this ecosystem is scarce and research on the ecosystem health is non-existent, even though ecosystem health is an useful indicator of the livelihood of an ecosystem. The goal of this research is to evaluate the health of the ecosystem through an assessment of Tor, I2P and GitHub. The Open Source Ecosystem Health Operationalization framework is used to help perform this assessment. Eight metrics from the framework are selected, which are measured using the data collected. Analysis of Tor and I2P metrics suggest that there has been an increase in Tor and I2P user activity in the recent past. Added knowledge, spin offs and forks and usage indicate active participation and interest in Tor and I2P. There has also been an increase in the number of active GitHub Dark Web projects. However, these GitHub projects are not well-connected and only a small number of projects have a large number of contributors. There is some variety among the GitHub software projects. The framework proves to be adequately capable of determining the health of the Dark Web open source ecosystem with the available data.

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Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development

Applied Sciences ◽

10.3390/app10134624 ◽

2020 ◽

Vol 10 (13) ◽

pp. 4624

Author(s):

Mitja Gradišnik ◽

Tina Beranič ◽

Sašo Karakatič

Keyword(s):

Open Source ◽

Open Source Software ◽

Software Maintenance ◽

Software Metrics ◽

Prediction Models ◽

Measurement Data ◽

Software Project ◽

Software Projects ◽

Software Metric ◽

Software Maintainability

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.

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The Impact of Project Initiators on Open Source Software Project Success

International Journal of Information Systems in the Service Sector ◽

10.4018/ijisss.2020010103 ◽

2020 ◽

Vol 12 (1) ◽

pp. 28-43

Author(s):

Ling Wang ◽

Jinxiao Wang

Keyword(s):

Open Source ◽

Open Source Software ◽

Project Success ◽

Software Project ◽

Software Projects ◽

Collective Behaviors ◽

Collective Wisdom ◽

Open Source Software Project ◽

Collaborative Innovation ◽

The Impact

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Anatomy of BioJS, an open source community for the life sciences

eLife ◽

10.7554/elife.07009 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 16

Author(s):

Guy Yachdav ◽

Tatyana Goldberg ◽

Sebastian Wilzbach ◽

David Dao ◽

Iris Shih ◽

...

Keyword(s):

Open Source ◽

Open Source Software ◽

Life Sciences ◽

Biological Data ◽

Software Project ◽

Software Projects ◽

Open Source Software Project ◽

Different Types ◽

Visualization Tools

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects.

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