Towards Mining OSS Skills from GitHub Activity

Liang, Jenny T.; Zimmermann, Thomas; Ford, Denae

doi:10.1109/icse-nier55298.2022.9793514

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…UW researchers contributed to ICSE 2022 in the areas of reliability and safety, SE communities, and mutation testing. Winston et al investigated repairing machine-learning-based brain-computer interfaces with fault localization [35], Liang et al explored mining OSS skills from GitHub [17], and Kaufman et al presented work on prioritizing mutants in mutation testing [13].…”

Section: Research From Uwmentioning

confidence: 99%

A Retrospective on ICSE 2022

Winston¹,

Caleb²,

Winston³

et al. 2022

Preprint

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Section: Research From Uwmentioning

confidence: 99%

A Retrospective on ICSE 2022

Winston¹,

Caleb²,

Winston³

et al. 2022

Preprint

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“…a) Language Profile: This signal aims at understanding the technical skills of OSS contributors [6]. For simplicity, we focused on the programming languages that each contributor used.…”

Section: B Signals Analyzed and Signal Scoringmentioning

confidence: 99%

“…But how do we keep projects like the aforementioned sustainable? Two approaches we have taken are to 1) detect and surface important skills it takes to be a successful contributor [6], [7] and 2) to help these projects retain the talent they already have [8]. Although these are helpful, there is still a key question we have yet to explore: how do we get projects access to the contributors they need to keep growing and evolving?…”

Section: Introductionmentioning

confidence: 99%

ReBOC: Recommending Bespoke Open Source Software Projects to Contributors

Ford

Shrestha

Zimmermann

2022

2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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Open Source Software for Social Good (OSS4SG) projects are projects that address a societal need and target people who need help. These projects often address high-impact humanitarian causes such as curating local health resources during a global pandemic, informing the public on the structural integrity of buildings, and encouraging civic engagement in times of strife. These projects carry a high intrinsic reward for contributing but are hard to find-prior research has shown that one of the the top challenges for contributors is not knowing where to find good projects to work on. Currently, contributors must manually search and assess whether projects align with their growing technical skills and intended impact interests.In this paper, we describe a recommendation system that automatically recommends OSS4SG projects for contributors based on their activity and project-related information. To score and rank projects, we calculated scores based on four signals: technical skills, interests, social ties, and recency of project activity. We performed an offline validation of the recommendation system using standard evaluation metrics such as the hit rate ratio. Results show that the signals are effective in producing a ranked list of OSS4SG projects for contributors, with room for improvement. Finally, we conducted a formative study with contributors to better understand their process of project discovery, validate our findings, and identify additional signals for future work to improve recommendations.

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Towards Mining OSS Skills from GitHub Activity

Cited by 2 publications

References 25 publications

A Retrospective on ICSE 2022

A Retrospective on ICSE 2022

ReBOC: Recommending Bespoke Open Source Software Projects to Contributors

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