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The Signals that Potential Contributors Look for When Choosing Open-source Projects

Open access
Qiu, Huilian Sophie, Li, Yucen Lily, Padala, Susmita, Sarma, Anita, Vasilescu, Bogdan·2019·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3359224
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Abstract

While open-source software has become ubiquitous, its sustainability is in question: without a constant supply of contributor effort, open-source projects are at risk. While prior work has extensively studied the motivations of open-source contributors in general, relatively little is known about how people choose which project to contribute to, beyond personal interest. This question is especially relevant in transparent social coding environments like GitHub, where visible cues on personal profile and repository pages, known as signals, are known to impact impression formation and decision making. In this paper, we report on a mixed-methods empirical study of the signals that influence the contributors' decision to join a GitHub project. We first interviewed 15 GitHub contributors about their project evaluation processes and identified the important signals they used, including the structure of the README and the amount of recent activity. Then, we proceeded quantitatively to test out the impact of each signal based on the data of 9,977 GitHub projects. We reveal that many important pieces of information lack easily observable signals, and that some signals may be both attractive and unattractive. Our findings have direct implications for open-source maintainers and the design of social coding environments, e.g., features to be added to facilitate better project searching experience.

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  • The number of GitHub stars is widely used when stakeholders evaluate, select, and adopt open-source projects into their software supply chain.

    “Many studies provided evidence that the number of GitHub stars is widely used when stakeholders evaluate, select, and adopt opensource projects into their software supply chain [33, 61, 71, 76, 89, 95].”

    From Six Million (Suspected) Fake Stars on GitHub: A Growing Spiral of Popularity Contests, Spams, and Malware (2026) · cites this paper as [76]

    Section 2.3 (page unavailable) reports that three out of four surveyed developers considered GitHub stars before using or contributing to a project. Section 4.7 (page unavailable) also describes stars as a popularity signal considered by interview participants. The paper does not establish that stars are widely used specifically to select and adopt projects into a software supply chain. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • There is evidence that the CoC's presence has no bearing on a newcomer's joining of a new project compared with other factors considered.

    “In fact, there is evidence that the CoC's presence has no bearing on a newcomer's joining of a new project compared with other factors considered [9,19].”

    From Codes of Conduct in Open Source (2024) · cites this paper as [19]

    Section 2.3 reports that a prior study ranked CODE OF CONDUCT files among the least informative factors for predicting growth in new committers. That does not establish that a code of conduct has no bearing on whether a newcomer joins, and the paper cautions that the prior study’s sampling may explain the weak predictive power. Its “Code of conduct” section also reports a participant who viewed one as a signal of a welcoming community. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • There is little to no empirical evidence to support that CoCs help attract newcomers.

    “CoCs are generally considered helpful in attracting newcomers [18,21], yet there is little to no empirical evidence to support that.”

    From Codes of Conduct in Open Source (2024) · cites this paper as [19]

  • CoC conversations are common in private in large projects with hierarchical structures.

    “If CoC conversations are done privately, common in large projects with hierarchical structures [3], leaders should make an effort to replicate that discussion publicly.”

    From Codes of Conduct in Open Source (2024) · cites this paper as [3]

    The “Code of conduct” section discusses a code of conduct as a signal of a welcoming community, while §4.8 discusses conversations visible in issues and pull requests. Neither addresses whether CoC conversations commonly happen privately in large projects with hierarchical structures (searched: private conversations, hierarchy, large projects). (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cites

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2 of the 83 references couldn’t be matched to a known paper.

Research works printed but not linked: 12

  1. Saeideh Bakhshi, Partha Kanuparthy, and David A. Shamma. 2015. Understanding Online Reviews: Funny, Cool or Useful?. In Proceedings of the ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW). ACM, 1270-1276.
  2. Benjamin C. Collier and Robert Hampshire. 2010. Sending Mixed Signals: Multilevel Reputation Eects in Peer-to-peer Lending Markets. In Proceedings of the ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW). ACM, 197-206.
  3. Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Daniel Jurafsky, Jure Leskovec, and Christopher Potts. 2013. A computational approach to politeness with application to social factors. meeting of the association for computational linguistics 1 (2013), 250-259.
  4. Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. A computational approach to politeness with application to social factors. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Vol. 1. 250-259.
  5. K Anders Ericsson and Herbert A Simon. 1980. Verbal reports as data. Psychological Review 87, 3 (1980), 215.
  6. William H Greene. 2003. Econometric analysis. Pearson Education India.
  7. Jerry A Hausman. 1978. Specication tests in econometrics. Econometrica: Journal of the Econometric Society (1978), 1251-1271.
  8. Christine M Liu and Judith S Donath. 2006. Urbanhermes: social signaling with electronic fashion. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 885-888.
  9. Xiao Ma, Jeery T. Hancock, Kenneth Lim Mingjie, and Mor Naaman. 2017. Self-Disclosure and Perceived Trustworthi- ness of Airbnb Host Proles. In Proceedings of the ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW). ACM, 2397-2409.
  10. Igor Steinmacher, Marco Aurélio Gerosa, and David Redmiles. 2014. Attracting, onboarding, and retaining newcomer developers in open source software projects. In Workshop on Global Software Development in a CSCW Perspective.
  11. Bogdan Vasilescu, Vladimir Filkov, and Alexander Serebrenik. 2015. Perceptions of Diversity on GitHub: A User Survey. In Proceedings of the International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE). IEEE, 50-56.
  12. Bogdan Vasilescu, Daryl Posnett, Baishakhi Ray, Mark G. J. van den Brand, Alexander Serebrenik, Premkumar Devanbu, and Vladimir Filkov. 2015. Gender and tenure diversity in GitHub teams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 3789-3798.

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Authors
Qiu, H. S., Li, Y. L., Padala, S., Sarma, A., & Vasilescu, B.
Affiliations
  • Carnegie Mellon University (US)
  • Oregon State University (US)
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2019-11-07
Volume
3
Issue
CSCW
Pages
1-29
ISSN
2573-0142 (ISSN-L)
DOI
10.1145/3359224
OpenAlex
W2986218565
Cited by
75
References
83
Citations by year
2026: 10 · 2025: 12 · 2024: 12 · 2023: 12 · 2022: 19 · 2021: 7 · 2020: 3
FWCI
7.85
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
Funders
  • National Science Foundation (1815486, 1901031, 1901311, 1717415)
  • Alfred P. Sloan Foundation
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
Keywords
open source software, GitHub, project evaluation, README, impression formation

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