arXiv subjects

Amanda Casari

Publications and source records attributed to Amanda Casari.

3 recordsLinked to original sources

arXiv2025

Invisible Labor in Open Source Software Ecosystems

Invisible labor is work that is either not fully visible or not appropriately compensated. In open source software (OSS) ecosystems, essential tasks that do not involve code (like content moderation) often become invisible to the detriment of individuals and organizations. However, invisible labor is sufficiently difficult to measure that we do not know how much of OSS activities are invisible. Our study addresses this challenge, demonstrating that roughly half of OSS work is invisible. We do this by developing a cognitive anchoring survey technique that measures OSS developer self-assessments of labor visibility and attribution. Survey respondents (n=142) reported that their work is more likely to be invisible (2 in 3 tasks) than visible, and that half (50.1%) is uncompensated. Priming participants with the idea of visibility caused participants to think their work was more visible, and that visibility was less important, than those primed with invisibility. We also found evidence that tensions between attribution motivations probably increase how common invisible labor is. This suggests that advertising OSS activities as "open" may lead contributors to overestimate how visible their labor actually is. Our findings suggest benefits to working with varied stakeholders to make select, collectively valued activities visible, and increasing compensation in valued forms (like attribution, opportunities, or pay) when possible. This could improve fairness in software development while providing greater transparency into work designs that help organizations and communities achieve their goals.

cs.SE↗

arXiv2022

The OCEAN mailing list data set: Network analysis spanning mailing lists and code repositories

Communication surrounding the development of an open source project largely occurs outside the software repository itself. Historically, large communities often used a collection of mailing lists to discuss the different aspects of their projects. Multimodal tool use, with software development and communication happening on different channels, complicates the study of open source projects as a sociotechnical system. Here, we combine and standardize mailing lists of the Python community, resulting in 954,287 messages from 1995 to the present. We share all scraping and cleaning code to facilitate reproduction of this work, as well as smaller datasets for the Golang (122,721 messages), Angular (20,041 messages) and Node.js (12,514 messages) communities. To showcase the usefulness of these data, we focus on the CPython repository and merge the technical layer (which GitHub account works on what file and with whom) with the social layer (messages from unique email addresses) by identifying 33% of GitHub contributors in the mailing list data. We then explore correlations between the valence of social messaging and the structure of the collaboration network. We discuss how these data provide a laboratory to test theories from standard organizational science in large open source projects.

cs.CY↗

arXiv2021

Which contributions count? Analysis of attribution in open source

Open source software projects usually acknowledge contributions with text files, websites, and other idiosyncratic methods. These data sources are hard to mine, which is why contributorship is most frequently measured through changes to repositories, such as commits, pushes, or patches. Recently, some open source projects have taken to recording contributor actions with standardized systems; this opens up a unique opportunity to understand how community-generated notions of contributorship map onto codebases as the measure of contribution. Here, we characterize contributor acknowledgment models in open source by analyzing thousands of projects that use a model called All Contributors to acknowledge diverse contributions like outreach, finance, infrastructure, and community management. We analyze the life cycle of projects through this model's lens and contrast its representation of contributorship with the picture given by other methods of acknowledgment, including GitHub's top committers indicator and contributions derived from actions taken on the platform. We find that community-generated systems of contribution acknowledgment make work like idea generation or bug finding more visible, which generates a more extensive picture of collaboration. Further, we find that models requiring explicit attribution lead to more clearly defined boundaries around what is and what is not a contribution.

cs.SE↗