Release synchronization in software ecosystems

Foundjem, Armstrong; Adams, Bram

doi:10.1007/s10664-020-09929-1

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…To guarantee that the codebook can be replicated, the second author deductively coded 20% [4,11] of the issues (41 issues). Afterwards, the first and third authors discussed the disagreements, improving our coding schema.…”

Section: 41mentioning

confidence: 99%

“…In the end, we added four TBDFs and adjusted the coding criteria of eight TBDFs in the original codebook. The third author then deductively coded 20% [4,11] of the comments coded with at least one TBDF by the first author (145 out of 718 comments). We measured the inter-rater reliability, and the average Cohen's Kappa score between the two raters and across all the identified TBDFs was 0.65, ranging from 0.43 to 0.91, showing substantial agreement between the two raters [31].…”

Section: 43mentioning

confidence: 99%

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How heated is it? Understanding GitHub locked issues

Ferreira,

Adams,

Cheng

2022

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Although issues of open source software are created to discuss and solve technical problems, conversations can become heated, with discussants getting angry and/or agitated for a variety of reasons, such as poor suggestions or violation of community conventions. To prevent and mitigate discussions from getting heated, tools like GitHub have introduced the ability to lock issue discussions that violate the code of conduct or other community guidelines. Despite some early research on locked issues, there is a lack of understanding of how communities use this feature and of potential threats to validity for researchers relying on a dataset of locked issues as an oracle for heated discussions. To address this gap, we (i) quantitatively analyzed 79 GitHub projects that have at least one issue locked as too heated, and (ii) qualitatively analyzed all issues locked as too heated of the 79 projects, a total of 205 issues comprising 5,511 comments. We found that projects have different behaviors when locking issues: while 54 locked less than 10% of their closed issues, 14 projects locked more than 90% of their closed issues. Additionally, locked issues tend to have a similar number of comments, participants, and emoji reactions to non-locked issues. For the 205 issues locked as too heated, we found that one-third do not contain any uncivil discourse, and only 8.82% of the analyzed comments are actually uncivil. Finally, we found that the locking justifications provided by maintainers do not always match the label used to lock the issue. Based on our results, we identified three pitfalls to avoid when using the GitHub locked issues data and we provide recommendations for researchers and practitioners. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing; • Software and its engineering → Open source model.

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Section: 41mentioning

confidence: 99%

Section: 43mentioning

confidence: 99%

How heated is it? Understanding GitHub locked issues

Ferreira,

Adams,

Cheng

2022

Self Cite

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A mixed-methods analysis of micro-collaborative coding practices in OpenStack

et al. 2022

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Technical collaboration between multiple contributors is a natural phenomenon in distributed open source software development projects. Macro-collaboration, where each code commit is attributed to a single collaborator, has been extensively studied in the research literature. This is much less the case for so-called micro-collaboration practices, in which multiple authors contribute to the same commit. To support such practices, GitLab and GitHub started supporting social coding mechanisms such as the “Co-Authored-By:” trailers in commit messages, which, in turn, enable to empirically study such micro-collaboration. In order to understand the mechanisms, benefits and limitations of micro-collaboration, this article provides an exemplar case study of collaboration practices in the OpenStack ecosystem. Following a mixed-method research approach we provide qualitative evidence through a thematic and content analysis of semi-structured interviews with 16 OpenStack contributors. We contrast their perception with quantitative evidence gained by statistical analysis of the git commit histories ( 1M commits) and Gerrit code review histories ( 631K change sets and 2M patch sets) of 1,804 OpenStack project repositories over a 9-year period. Our findings provide novel empirical insights to practitioners to promote micro-collaborative coding practices, and to academics to conduct further research towards understanding and automating the micro-collaboration process.

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