GitHub marketplace for automation and innovation in software production
Bibliographic record
Abstract
Context: GitHub, renowned for facilitating collaborative code version control and software production in software teams, expanded its services in 2017 by introducing GitHub Marketplace. This online platform hosts automation tools to assist developers with the production of their GitHub-hosted projects, and it has become a valuable source of information on the tools used in the Open Source Software (OSS) community. Objective: In this exploratory study , we introduce GitHub Marketplace as a software marketplace by exploring the Characteristics, Features, and Policies of the platform comprehensively, identifying common themes in production automation. Further, we explore popular tools among practitioners and researchers and highlight disparities in the approach to these tools between industry and academia. Method: We adopted the conceptual framework of software app stores from previous studies and used that to examine 8,318 automated production tools (440 Apps and 7,878 Actions) across 32 categories on GitHub Marketplace. We explored and described the policies of this marketplace as a unique platform where developers share production tools for the use of other developers. Furthermore, we conducted a systematic mapping of 515 research papers published from 2000 to 2021 and compared open-source academic production tools with those available in the marketplace. Results: We found that although some of the automation topics in literature are widely used in practice, they have yet to align with the state-of-practice for automated production. We discovered that practitioners often use automation tools for tasks like “Continuous Integration” and “Utilities”, while researchers tend to focus more on “Code Quality” and “Testing”. Conclusion: Our study illuminates the landscape of open-source tools for automation production. We also explored the disparities between industry trends and researchers’ priorities. Recognizing these distinctions can empower researchers to build on existing work and guide practitioners in selecting tools that meet their specific needs. Bridging this gap between industry and academia helps with further innovation in the field and ensures that research remains pertinent to the evolving challenges in software production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".