This study examines the influence of social scoring mechanisms on the contributions made by users on the popular software development platform, GitHub. The advent of online collaborative platforms has revolutionized the way software is developed, allowing developers to collaborate, share, and contribute to projects in a decentralized manner. GitHub, in particular, has become a prominent platform for open source software development, hosting millions of repositories and facilitating collaboration among developers from diverse backgrounds.
Social scoring, a feature introduced by GitHub, aims to provide an assessment of a user’s contributions and reputation within the community. This scoring system assigns users a score based on various factors, such as the quality and quantity of their code contributions, their interactions with other users, and their overall engagement in the community. However, the impact of such social scoring mechanisms on users’ behavior and their contributions to the platform remains largely unexplored.
This study adopts a mixed-methods approach to investigate the effects of social scoring on GitHub contributions. Using a dataset comprising anonymized user activities, including code contributions, interactions, and reputation scores, we analyze the relationship between social scoring and users’ behavior on GitHub. We employ statistical modeling techniques to quantify the influence of social scoring while controlling for relevant factors, such as users’ experience, project characteristics, and community dynamics.
Additionally, we conduct qualitative interviews with users to gain insights into their perceptions and experiences regarding social scoring on GitHub. The interviews explore how users understand and interpret their reputation scores, how these scores influence their motivation and sense of belonging, and whether they perceive any potential biases in the social scoring system.
The findings of this study will contribute to our understanding of how social scoring mechanisms impact users’ behavior on collaborative platforms like GitHub. By uncovering the effects of social scoring, we aim to inform the design of more inclusive and equitable evaluation systems for online communities, particularly those focused on software development.
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