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OSS projects are being developed by globally distributed contributors, who often collaborate through the pull-based model today. While this model lowers the barrier to entry for OSS developers by synthesizing, automating and optimizing the contribution process, coordination among an increasing number of contributors remains as a challenge due to the asynchronous and self-organized nature of distributed development. In particular, duplicate contributions, where multiple different contributors unintentionally submit duplicate pull requests to achieve the same goal, are an elusive problem that may waste effort in automated testing, code review and software maintenance. While the issue of duplicate pull requests has been highlighted, to what extent duplicate pull requests affect the development in OSS communities has not been well investigated. In this paper, we conduct a mixed-approach study to bridge this gap. Based on a comprehensive dataset constructed from 26 popular GitHub projects, we obtain the following findings: (a) Duplicate pull requests result in redundant human and computing resources, exerting a significant impact on the contribution and evaluation process. (b) Contributors' inappropriate working patterns and the drawbacks of their collaborating environment might result in duplicate pull requests. (c) Compared to non-duplicate pull requests, duplicate pull requests have significantly different features, e.g., being submitted by inexperienced contributors, being fixing bugs, touching cold files, and solving tracked issues. (d) Integrators choosing between duplicate pull requests prefer to accept those with early submission time, accurate and high-quality implementation, broad coverage, test code, high maturity, deep discussion, and active response. Finally, actionable suggestions and implications are proposed for OSS practitioners.

Commercial participation continues to grow in open source software (OSS) projects and novel arrangements appear to emerge in company-dominated projects and ecosystems. What is the nature of these novel arrangements? Does volunteers’ participation remain critical for these ecosystems? Despite extensive research on commercial participation in OSS, the exact nature and extent of company contributions to OSS development, and the impact of this engagement may have on the volunteer community have not been clarified. To bridge the gap, we perform an exploratory study of OpenStack: a large OSS ecosystem with intense commercial participation. We quantify companies’ contributions via the developers that they provide and the commits made by those developers. We find that companies made far more contributions than volunteers and the distribution of the contributions made by different companies is also highly unbalanced. We observe eight unique contribution models based on companies’ commercial objectives and characterize each model according to three dimensions: contribution intensity, extent, and focus. Companies providing full cloud solutions tend to make both intensive (more than other companies) and extensive (involving a wider variety of projects) contributions. Usage-oriented companies make extensive but less intense contributions. Companies driven by particular business needs focus their contributions on the specific projects addressing these needs. Minor contributors include community players (e.g., the Linux Foundation) and research groups. A model relating the number of volunteers to the diversity of contribution shows a strong positive association between them.

Motivation: To survive and succeed, FLOSS projects need contributors able to accomplish critical project tasks. However, such tasks require extensive project experience of long term contributors (LTCs). Aim: We measure, understand, and predict how the newcomers’ involvement and environment in the issue tracking system (ITS) affect their odds of becoming an LTC. Method: ITS data of Mozilla and Gnome, literature, interviews, and online documents were used to design measures of involvement and environment. A logistic regression model was used to explain and predict contributor’s odds of becoming an LTC. We also reproduced the results on new data provided by Mozilla. Results: We constructed nine measures of involvement and environment based on events recorded in an ITS. Macro-climate is the overall project environment while micro-climate is person-specific and varies among the participants. Newcomers who are able to get at least one issue reported in the first month to be fixed, doubled their odds of becoming an LTC. The macro-climate with high project popularity and the micro-climate with low attention from peers reduced the odds. The precision of LTC prediction was 38 times higher than for a random predictor. We were able to reproduce the results with new Mozilla data without losing the significance or predictive power of the previously published model. We encountered unexpected changes in some attributes and suggest ways to make analysis of ITS data more reproducible. Conclusions: The findings suggest the importance of initial behaviors and experiences of new participants and outline empirically-based approaches to help the communities with the recruitment of contributors for long-term participation and to help the participants contribute more effectively. To facilitate the reproduction of the study and of the proposed measures in other contexts, we provide the data we retrieved and the scripts we wrote at https://www.passion-lab.org/projects/developerfluency.html.