Modeling the impact of Python and R packages using dependency and contributor networks

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

doi:10.1007/s13278-019-0619-1

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…Our findings suggest standard accounting measures underestimate the existing stock of IT due to the omission of unpriced IT, that is, IT acquired at a zero price. This conclusion also aligns with Korkmaz et al (2019) Third, we document previously unrecognized patterns of misattribution correlated with the use of server software. As with Greenstein and Nagle (2014), this study indicates that the benefits from federal support for the internet have been underestimated.…”

Section: Introductionsupporting

confidence: 87%

Hidden Software and Veiled Value Creation: Illustrations from Server Software Usage

Murciano-Goroff¹,

Zhuo²,

Greenstein³

2021

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How do you measure the value of a commodity that transacts at a price of zero from an economic standpoint? This study examines the potential for and extent of omission and misattribution in standard approaches to economic accounting with regards to open source software, an unpriced commodity in the digital economy. The study is the first to follow usage and upgrading of unpriced software over a long period of time. It finds evidence that software updates mislead analyses of sources of firm productivity and identifies several mechanisms that create issues for mismeasurement. To illustrate these mechanisms, this study closely examines one asset that plays a critical role in the digital economic activity, web server software. We analyze the largest dataset ever compiled on web server use in the United States and link it to disaggregated information on over 200,000 medium to large organizations in the United States between 2001 and 2018. In our sample, we find that the omission of economic value created by web server software is substantial and that this omission indicates there is over $4.5 billion dollars of mismeasurement of server software across organizations in the United States. This mismeasurement varies by organization age, geography, industry and size. We also find that dynamic behavior, such as improvements of server technology and entry of new products, further exacerbates economic mismeasurement.

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Section: Introductionsupporting

confidence: 87%

Hidden Software and Veiled Value Creation: Illustrations from Server Software Usage

Murciano-Goroff¹,

Zhuo²,

Greenstein³

2021

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“…This intersection also provides the reference point for the present work; the paper continues the recent large-scale empirical analyses on software security issues in software ecosystems [22], [25], [82]. As is elaborated in the opening Section II, the paper also belongs to a specific corner in this research branch: the packages within PyPI are analyzed in isolation of each other; the ecosystem concept is understood in statistical terms as a population rather than in technical terms as a collection of more or less interlinked packages.…”

Section: Introductionmentioning

confidence: 74%

“…Despite the necessary exclusions discussed, the dataset constructed contains the static analysis results for as many as n = 197, 726 packages. Thus, the size of the dataset is very similar to other recent large-scale studies (for instance, 192, 666 Python packages were retrieved from PyPI in [22]). A brief remark is also in order about the tool providing the results in the present work.…”

Section: Fig 1: Sample Construction In a Nutshellmentioning

confidence: 80%

“…That said, software package repositories (such as npm for JavaScript, CPAN for Perl, or CRAN for R) are likely the most studied software ecosystems in empirical research. Also PyPI has been studied recently [22], [55], [78]. In light of the definition, the Python Package Index indeed provides a common environment, although the actual development mainly occurs on GitHub, another software ecosystem.…”

Section: B Related Workmentioning

confidence: 99%

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A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI

Ruohonen,

Hjerppe,

Rindell

2021

Preprint

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Different security issues are a common problem for open source packages archived to and delivered through software ecosystems. These often manifest themselves as software weaknesses that may lead to concrete software vulnerabilities. This paper examines various security issues in Python packages with static analysis. The dataset is based on a snapshot of all packages stored to the Python Package Index (PyPI). In total, over 197 thousand packages and over 749 thousand security issues are covered. Even under the constraints imposed by static analysis, (a) the results indicate prevalence of security issues; at least one issue is present for about 46% of the Python packages. In terms of the issue types, (b) exception handling and different code injections have been the most common issues. The subprocess module stands out in this regard. Reflecting the generally small size of the packages, (c) software size metrics do not predict well the amount of issues revealed through static analysis. With these results and the accompanying discussion, the paper contributes to the field of large-scale empirical studies for better understanding security problems in software ecosystems.

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Understanding Roxygen package documentation in R

Vidoni

2022

Journal of Systems and Software

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Modeling the impact of Python and R packages using dependency and contributor networks

Cited by 10 publications

References 27 publications

Hidden Software and Veiled Value Creation: Illustrations from Server Software Usage

Hidden Software and Veiled Value Creation: Illustrations from Server Software Usage

A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI

Understanding Roxygen package documentation in R

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