We found that nearly half of the research investigates the structure of developer communities. We mapped the primary studies to research directions, collected information about the data sources and the size of the studies, and conducted a bibliometric assessment. We identified 255 primary studies on DSNs.
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Within this paper, we present the results of a systematic mapping study on the use of DSNs in software engineering research. Finally it is argued that the Wikipedian monitoring of new edits, especially by its heavy reliance on computational tools, raises a number of moral questions that need to be answered urgently.ĭeveloper social networks (DSNs) are a tool for the analysis of community structures and collaborations between developers in software projects and software ecosystems. Further, the essential differences between backgrounding and substituting trust are elaborated. These collective monitoring efforts are interpreted as focusing on avoiding possible damage being inflicted on Wikipedian spaces, thereby being allowed to keep the discretionary powers of editing intact for all users.
Measures of reputation are also under investigation within Wikipedia their incorporation in monitoring efforts, as an indicator of the trustworthiness of editors, is envisaged. Computational approaches have been developed for the purpose, yielding both sophisticated monitoring tools that are used by human patrollers, and bots that operate autonomously.
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full write-access) is ‘backgrounded’ by means of a permanent mobilization of Wikipedians to monitor incoming edits. In the particular case of Wikipedia, which is confronted with persistent vandalism, another arrangement has been pioneered instead. Encyclopedic communities, though, largely avoid this solution. It is shown that communities of open-source software-continue to-rely mainly on hierarchy (reserving write-access for higher echelons), which substitutes (the need for) trust. This research investigates the various ways in which these communities manage this issue. Open-content communities that focus on co-creation without requirements for entry have to face the issue of institutional trust in contributors. Further, we develop a pull request classifier that exploits trust metrics to effectively predict the likelihood of a pull request being accepted to a project, demonstrating the practical utility of our approach. A large-scale evaluation of our approach on a GitHub dataset consisting of 24,315 developers shows that contributions from trusted developers are more likely to be accepted to a project compared to contributions from developers who are distrusted or lacking trust from project members. Second, we infer indirect trust between developers who have not interacted previously by constructing a community-wide developer network and propagating trust in the network. Our two-fold approach, first, computes direct trust between developer pairs who have interacted previously by analyzing their interactions via natural language processing.
We propose an automated approach to assist a developer in identifying the trustworthiness of another developer. Consequently, OSS project members must rely on subjective inferences based on fragile and incomplete information for trust-related decision making. Although existing research recognizes the importance of trust, there is a lack of an effective and scalable computational method to measure trust in an OSS community. Trust between developers influences the success of open source software (OSS) projects.
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