How the R community creates and curates knowledge

Zagalsky, Alexey; Teshima, Carlos Gómez; Germán, Daniel M.; Storey, Margaret-Anne; Poo-Caamaño, Germán

doi:10.1145/2901739.2901772

Cited by 21 publications

(5 citation statements)

References 20 publications

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“…Extracting data from multiple sources [14] Require human interpretation to map data across sources [14] Consistent preprocessing of data [3] adapters can be extended easily using ImportAdapter component for other sources shown in Table I.…”

Section: Fw Combining Sourcesmentioning

confidence: 99%

“…-In natural language processing, the user can apply word stemming, 13 remove all stop words, 14 or remove all punctuation.…”

Section: Fw Combining Sourcesmentioning

confidence: 99%

“…However, they lack the ability to manually explore, extract, and map the data from various sources as well as to investigate small samples, or perform ad-hoc searches on intermediate data. Researchers interested in using new sources of data or combining multiple sources using various heuristics to map the sources [14] with state-of-art-tools are limited in their decision making. Human interpretation, ad-hoc testing of simple hypotheses, rescaling a dataset, or the assessment of data quality is often required to determine the plausible approach or methodology for using the new data sources and combining the multiple sources.…”

Section: Multi-source Analysis Using Makarmentioning

confidence: 99%

See 2 more Smart Citations

Makar: A Framework for Multi-source Studies based on Unstructured Data

Birrer

Rani

Panichella

et al. 2021

2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Section: Fw Combining Sourcesmentioning

confidence: 99%

“…-In natural language processing, the user can apply word stemming, 13 remove all stop words, 14 or remove all punctuation.…”

Section: Fw Combining Sourcesmentioning

confidence: 99%

Section: Multi-source Analysis Using Makarmentioning

confidence: 99%

See 1 more Smart Citation

Makar: A Framework for Multi-source Studies based on Unstructured Data

Birrer

Rani

Panichella

et al. 2021

2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…Zagalsky et al [31] have analyzed the R language using data from both Stack Overflow and R-help mailing list. They focused on the participation pattern of users in the two communities.…”

Section: Related Workmentioning

confidence: 99%

“…Vasilescu et al [32] have compared popularity and user activity level between StackOverflow and R-help mailing list. They followed a similar approach to Zagalsky et al [31] by identifying active users in both communities. They have some interesting findings on the decreasing popularity of the mailing list and the influence of the reputation system in Stack Overflow.…”

Section: Related Workmentioning

confidence: 99%

How do developers discuss and support new programming languages in technical Q&A site? An empirical study of Go, Swift, and Rust in Stack Overflow

Chakraborty,

Shahriyar,

Iqbal

et al. 2021

Preprint

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Context: New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced to provide a better opportunity for the developers to make software development robust and easy. At the early stage, a programming language is likely to have resource constraints that encourage the developers to seek help frequently from experienced peers active in Question-Answering (QA) sites such as Stack Overflow (SO). Objective: In this study, we have formally studied the discussions on three popular new languages introduced after the inception of SO (2008) and match those with the relevant activities in GitHub whenever appropriate. For that purpose, we have mined 4,17,82,536 questions and answers from SO and 7,846 issue information along with 6,60,965 repository information from Github. Initially, the development of new languages is relatively slow compared to mature languages (e.g., C, C++, Java). The expected outcome of this study is to reveal the difficulties and challenges faced by the developers working with these languages so that appropriate measures can be taken to expedite the generation of relevant resources. Method: We have used the Latent Dirichlet Allocation (LDA) method on SO's questions and answers to identify different topics of new languages. We have extracted several features of the answer pattern of the new languages from SO (e.g., time to get an accepted answer, time to get an answer, etc.) to study their characteristics. These attributes were used to identify difficult topics. We explored the background of developers who are contributing to these languages.We have created a model by combining Stack Overflow data and issues, repository, user data of Github. Finally, we have used that model to identify factors that affect language evolution. Results: The major findings of the study are: (i) migration, data and data structure are generally the difficult topics of new languages, (ii) the time when adequate resources are expected to be available vary from language to language, (iii) the unanswered question ratio increases regardless of the age of the language, and (iv) there is a relationship between developers' activity pattern and the growth of a language. Conclusion: We believe that the outcome of our study is likely to help the owner/sponsor of these languages to design better features and documentation. It will also help the software developers or students to prepare themselves to work on these languages in an informed way.

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The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries

Geiger

Varoquaux

Mazel-Cabasse

et al. 2018

Comput Supported Coop Work

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Computational research and data analytics increasingly relies on complex ecosystems of open source software (OSS) "libraries" -curated collections of reusable code that programmers import to perform a specific task. Software documentation for these libraries is crucial in helping programmers/analysts know what libraries are available and how to use them. Yet documentation for open source software libraries is widely considered low-quality. This article is a collaboration between CSCW researchers and contributors to data analytics OSS libraries, based on ethnographic fieldwork and qualitative interviews. We examine several issues around the formats, practices, and challenges around documentation in these largely volunteer-based projects. There are many different kinds and formats of documentation that exist around such libraries, which play a variety of educational, promotional, and organizational roles. The work behind documentation is similarly multifaceted, including writing, reviewing, maintaining, and organizing documentation. Different aspects of documentation work require contributors to have different sets of skills and overcome various social and technical barriers. Finally, most of our interviewees do not report high levels of intrinsic enjoyment for doing documentation work (compared to writing code). Their motivation is affected by personal and project-specific factors, such as the perceived level of credit for doing documentation work versus more 'technical' tasks like adding new features or fixing bugs. In studying documentation work for data analytics OSS libraries, we gain a new window into the changing practices of data-intensive research, as well as help practitioners better understand how to support this often invisible and infrastructural work in their projects.

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How the R community creates and curates knowledge

Cited by 21 publications

References 20 publications

Makar: A Framework for Multi-source Studies based on Unstructured Data

Makar: A Framework for Multi-source Studies based on Unstructured Data

How do developers discuss and support new programming languages in technical Q&A site? An empirical study of Go, Swift, and Rust in Stack Overflow

The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries

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