Impression Formation in Commons-based Peer Production: Activity Traces and Personal Profiles in GitHub

This post originally appeared in the  Follow the Crowd blog and describes an upcoming paper at CSCW 2013

In this paper we describe an investigation of impression formation in GitHub, an online distributed software development community with social media functionality. We find that users in this setting seek out additional information about each other to explore the project space, inform future interactions, and understand the potential future value of a new contributor. They form impressions around other users’ expertise and attitude based on visible history of activity across projects and successful collaborations with key high status projects in the community, and use this information to inform a cost-benefit analysis guiding decisions about how to interact with contributors to their projects.

The open source software development domain presents an example of commons-based peer production where uncertainty about the quality of others is the rule rather than the exception.  The issue of deciding how to deal with problematic code contributions can partially be attributed to this uncertain environment, where project owners may have difficulty understanding the expertise, background, and credentials of unknown contributors who seek to become involved.

However, with the advent of open workspaces instrumented with social media functionalities, there is now the potential to access information about contributors’ expertise, work style and interaction history.  This allows the potential to understand what other people are working on and how they interact with others just by looking at their profiles.  Several unique aspects of the GitHub environment provide data about the actions of members of the community not available in traditional open software repositories or other online peer production settings.

We interviewed project owners on GitHub to investigate how they used profile information to decide whether or not to accept a pull request (code contribution) from unknown contributors.  Owners described the various profile cues they attended to and the inferences about ability and attitude that they drew from this information.

Overall, we found that profile information was not necessarily useful in cases where the benefit and value of the code was evident.  However, when the code was problematic and not immediately acceptable, some developers used the contributors’ profiles to form quick impressions of their abilities and attitudes, which then helped them to form a cost-benefit analysis guiding further interaction.

For example, the cost of working with someone to fix their code so it could be accepted (which could be high in cases where contributors were newcomers or novices) was weighed against the potential benefits of helping mentor a new project member and potentially gaining help in the future, or the risks of being annoyed by time-consuming arguments with a novice whose profile showed evidence of a poor working attitude in the past.

The results show promise for the design of future systems to support distributed, computer-supported work.  By providing collaborators with more visible and easily-ascertained cues about a person’s abilities and work style, the impression formation process may be expedited and lead to more awareness of abilities and the effort involved in accepting the contribution.  This can potentially foster positive social outcomes in cases where there is a perceived gap in skill levels between the owner and contributor and the technical merits of the work are unclear and up for debate.

Jennifer Marlow, Human-Computer Interaction Institute, Carnegie Mellon University
Laura Dabbish, Human-Computer Interaction Institute and Center for the Future of Work, Heinz College, Carnegie Mellon University

Jim Herbsleb, Institute for Software Research and Center for the Future of Work, Carnegie Mellon University


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