Naomichi Shimada

Naomichi Shimada, Tao Xiao, Hideaki Hata et al.

GitHub Sponsors, launched in 2019, enables donations to individual open source software (OSS) developers. Financial support for OSS maintainers and developers is a major issue in terms of sustaining OSS projects, and the ability to donate to individuals is expected to support the sustainability of developers, projects, and community. In this work, we conducted a mixed-methods study of GitHub Sponsors, including quantitative and qualitative analyses, to understand the characteristics of developers who are likely to receive donations and what developers think about donations to individuals. We found that: (1) sponsored developers are more active than non-sponsored developers, (2) the possibility to receive donations is related to whether there is someone in their community who is donating, and (3) developers are sponsoring as a new way to contribute to OSS. Our findings are the first step towards data-informed guidance for using GitHub Sponsors, opening up avenues for future work on this new way of financially sustaining the OSS community.

Noppadol Assavakamhaenghan, Supatsara Wattanakriengkrai, Naomichi Shimada et al.

Open Source Software (OSS) projects rely on a continuous stream of new contributors for their livelihood. Recent studies reported that new contributors experience many barriers in their first contribution, with the social barrier being critical. Although a number of studies investigated the social barriers to new contributors, we hypothesize that negative first responses may cause an unpleasant feeling, and subsequently lead to the discontinuity of any future contribution. We execute protocols of a registered report to analyze 2,765,917 first contributions as Pull Requests (PRs) with 642,841 first responses. We characterize most first response as being positive, but less responsive, and exhibiting sentiments of fear, joy and love. Results also indicate that negative first responses have the literal intention to arouse emotions of being either constructive (50.71%) or criticizing (37.68%) in nature. Running different machine learning models, we find that predicting future interactions is low (F1 score of 0.6171), but relatively better than baselines. Furthermore, an analysis of these models show that interactions are positively correlated with a future contribution, with other dimensions (i.e., project, contributor, contribution) having a large effect.