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Record W3089977198 · doi:10.1145/3377812.3382165

An empirical study of the first contributions of developers to open source projects on GitHub

2020· article· en· W3089977198 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsJavaOpen sourcePopularityComputer scienceOpen source softwareSource codeEmpirical researchWorld Wide WebCode (set theory)SoftwareSoftware engineeringJoin (topology)DatabaseProgramming languagePolitical science

Abstract

fetched live from OpenAlex

The popularity of Open Source Software (OSS) is at an all-time high and for it to remain so it is vital for new developers to continually join and contribute to the OSS community. In this paper, to better understand the first time contributor, we study the characteristics of the first pull request (PR) made to an OSS project by developers. We mine GitHub for the first OSS PR of 3501 developers to study certain characteristics of PRs like language and size. We find that over 1/3rd of the PRs were in Java while C++ was very unpopular. A large fraction of PRs didn't even involve writing code, and were a mixture of trivial and non-trivial changes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.350
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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