Modeling the Impact of R Packages Using Dependency and Contributor Networks

Korkmaz, Gizem; Kelling, Claire; Robbins, Carol A.; Keller‐McNulty, Sallie

doi:10.1109/asonam.2018.8508255

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…Finally, outDegree has a strong positive influence on the number of downloads. This finding aligns with cross-sectional results in past studies [5]. From a communication perspective, in the OSS context, outDegree reflects the reuse of information provided by other packages.…”

Section: Resultssupporting

confidence: 90%

“…All the centrality measures that we have included in our study have a significant effect on the dependent variable except for Page Rank (PR). This result echoes the findings reported in [5], and can be explained by looking at the definition of this centrality measure. Page Rank [33] is designed to reflect a global ranking of all web pages based solely on their location in the network.…”

Section: Discussionsupporting

confidence: 90%

“…As proposed by Korkmaz, OSS development for scientific research is closely related to the social network of collaborative production [5]. Indeed, patterns of contribution and interaction among the contributors' network are crucial in explaining the success of FLOSS projects [15].…”

Section: Related Workmentioning

confidence: 99%

“…Third, the network dependency factor reflects the chain of reuse of a software, thus measuring the indirect contributions to a research. From a network structure perspective, Korkmaz and Kelling [5] propose an approach that focuses on the relationship between centrality measures in coauthorship networks and scientific productivity [13]. They show that network measures, such as indegree, outdegree, closeness centrality, betweenness, eigencentrality, and clustering coefficient, are significantly associated with number of downloads and citations in both packages' dependency network and contributor social network.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, it tracks not only citations within academic literature, but also alternative metrics such as number of downloads, software reuse through reverse dependencies, and contributors to the OSS. Their dataset facilitates the creation of contributors and dependencies networks that, in turn, allows one to estimate or quantify the impact of the packages' network features on the performance, namely number of downloads and citations [5,6]. Although datasets like this have assisted researchers to obtain some interesting results [5,7], past research lacks the longitudinal perspective to have causal relationship between package attributes and performance (since such a causal relationship may take a long time to realize).…”

Section: Introductionmentioning

confidence: 99%

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A Social Network Perspective on the Success of Open Source Software: The Case of R Packages

Zanella¹,

Liu²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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In this paper, we seek to identify the factors that influence the impact of open source software (OSS) on users community through the analysis of the evolution of the OSS network. Based on longitudinal data collected from the comprehensive R archive network (CRAN), we empirically examine how the network of R packages evolves over time and exert its influence on the scientific community. We find that critical network features derived from CRAN, such as pagerank, closeness, and betweenness centralities, play a significant role in determining the impact of each package on the research and publication activities in the scientific community. Furthermore, the performance of R packages can be explained as a flow of information from the core to the periphery that exhibits strong spillover effects. (CC BY-NC-ND 4.0) probability for actors to be included into an exchange of information which, in turn, is instrumental in assessing the level of influence of each node in the communications at a local level and across the whole network. The directional patterns of the communication describe how information moves around and how much actors can facilitate or control the flow. A number of aspects of information can be studied using approaches in social network analysis, including information needs, information exposure, information flow, information control, and information opportunities [22]. As discussed in the introduction, the major drawback of the social network approach lies in its cyclic nature. In our case, a cyclic network characterization is not possible due to the nature of the relationships between packages. In

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Section: Resultssupporting

confidence: 90%

Section: Discussionsupporting

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Social Network Perspective on the Success of Open Source Software: The Case of R Packages

Zanella¹,

Liu²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Towards a taxonomy of Roxygen documentation in R packages

Vidoni

Codabux

2023

Empir Software Eng

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Software documentation is often neglected, impacting maintenance and reuse and leading to technical issues. In particular, when working with scientific software, such issues in the documentation pose a risk to producing reliable scientific results as they may cause improper or incorrect use of the software. R is a popular programming language for scientific software with a prolific package-based ecosystem, where users contribute packages (i.e., libraries). R packages are intended to be reused, and their users rely extensively on the available documentation. Thus, understanding what information developers provide in their packages’ documentation (generally, through a system known as Roxygen, based on Javadoc) is essential to contribute to it. This study mined 379 GitHub repositories of R packages and analysed a sample to develop a taxonomy of natural language descriptions used in Roxygen documentation. This was done through hybrid card sorting, which included two experienced R developers. The resulting taxonomy covers parameters, returns, and descriptions, providing a baseline for further studies. Our taxonomy is the first of its kind for R. Based on previous studies in pure object-oriented languages, our taxonomy could be extensible to other dynamically-typed languages used in scientific programming.

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