A study of external community contribution to open-source projects on GitHub

Padhye, Rohan; Mani, Senthil; Sinha, Vibha Singhal

doi:10.1145/2597073.2597113

Cited by 54 publications

(18 citation statements)

References 4 publications

(4 reference statements)

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“…Rather recent research has showed interest in the investigation of pull request-based Software development [5,8,9,10]. Pull request is a process through which an external or internal collaborator contributes in a project stored in software repositories managed by a DVCS.…”

Section: Pull Request-based Software Developmentmentioning

confidence: 99%

Acceptance factors of pull requests in open-source projects

Soares

Júnior

Murta

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Distributed version control systems provide support for pull request strategy, which is used to register external contributions in collaborative software projects. The data present on a pull request can provide insights of factors that have influence on the acceptance or rejection of contributions in open source projects. Furthermore, the discovery of knowledge about pull requests allows confirming or denying existing hypotheses and helps software developers and project managers to guide their actions. This work proposes the use of data mining, more specifically, the extraction of association rules, to find patterns that exert influence on the acceptance (merge) of a pull request. The results suggest that: (i) the use of association rules allows to identify which factors increase the likelihood of a pull request merge; (ii) the identification of attributes that influence the merge reveals important knowledge about the pull request model; and (iii) with the use of association rules, it is possible to determine which factors contribute to a faster merge.

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Section: Pull Request-based Software Developmentmentioning

confidence: 99%

Acceptance factors of pull requests in open-source projects

Soares

Júnior

Murta

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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“…One of the conclusions of Padhye et al [14] is that only 15% of fork projects contain at least one commit. This is strong support for using "forker" and "fork committer" as two roles that are usually ignored in other research.…”

Section: Threats To Validitymentioning

confidence: 97%

“…In GitHub there are two types of projects: root projects (or called first repositories), those that are not forks of other repositories, and fork projects, those that are forks of other repositories [14] [15]. If a developer is interested in a root project, he could fork it and works on the fork project; the code modifications in the fork project can be pushed back to the root project by the pull-request and merge mechanisms; and consequently, his work is merged with the one of other developers of the root project.…”

Section: A Ecosystems Of Floss Projectsmentioning

confidence: 99%

“…Padhye et al [14] studied the openness of FLOSS projects that cover three issues called community composition, external contribution analysis, and time to become collaborators. The first issue addresses a similar question as RQ1, and the last issue can be considered as the role transformation from outer-layer developers to the members or coordinators of the projects, which is part of our study in RQ2.…”

Section: Threats To Validitymentioning

confidence: 99%

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Role Distribution and Transformation in Open Source Software Project Teams

Wang

Perry

2015

2015 Asia-Pacific Software Engineering Conference (APSEC)

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In Free/Libre and Open-Source Software (FLOSS) project teams, different roles make different types of contributions to the projects, and it has been demonstrated that keeping a rational role distribution is of great significance to the potential growth and expansion of the projects. To identify the underlying patterns of team structures and role transformations, we perform an empirical study on 89 popular GitHub projects. The distribution of 9 roles and 66 types of role transformations are analyzed. Four clusters of FLOSS projects are identified in terms of the distinct characteristics on the proportion of 9 roles (i.e., the team structure) and the frequency of occurrence, duration, and Times of Activeness (ToA) of role transformations. This study would help FLOSS project coordinator have a deeper understanding of dynamic role distributions and role transformations in their teams, so as to take initiatives to improve their team structures.

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“…GitHub Stargazers (e.g. Padhye et al (2014), Ray et al (2014), Casalnuovo et al (2015), Silva et al (2016), and Russell et al (2018)). The rationale behind using popularity as a filtering criterion is that it is assumed to be positively correlated with quality.…”

Section: Related Workmentioning

confidence: 99%

PHANTOM: Curating GitHub for engineered software projects using time-series clustering

et al. 2020

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Context Within the field of Mining Software Repositories, there are numerous methods employed to filter datasets in order to avoid analysing low-quality projects. Unfortunately, the existing filtering methods have not kept up with the growth of existing data sources, such as GitHub, and researchers often rely on quick and dirty techniques to curate datasets.Objective The objective of this study is to develop a method capable of filtering large quantities of software projects in a resource-efficient way.Method This study follows the Design Science Research (DSR) methodology. The proposed method, PHANTOM, extracts five measures from Git logs. Each measure is transformed into a time-series, which is represented as a feature vector for clustering using the k-means algorithm. ResultsUsing the ground truth from a previous study, PHANTOM was shown to be able to rediscover the ground truth on the training dataset, and was able to identify "engineered" projects with up to 0.87 Precision and 0.94 Recall on the validation dataset. PHANTOM downloaded and processed the metadata of 1,786,601 GitHub repositories in 21.5 days using a single personal computer, which is over 33% faster than the previous study which used a computer cluster of 200 nodes. The possibility of applying the method outside of the opensource community was investigated by curating 100 repositories owned by two companies.Conclusions It is possible to use an unsupervised approach to identify engineered projects. PHANTOM was shown to be competitive compared to the existing supervised approaches while reducing the hardware requirements by two orders of magnitude.

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A study of external community contribution to open-source projects on GitHub

Cited by 54 publications

References 4 publications

Acceptance factors of pull requests in open-source projects

Acceptance factors of pull requests in open-source projects

Role Distribution and Transformation in Open Source Software Project Teams

PHANTOM: Curating GitHub for engineered software projects using time-series clustering

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