From First Use to Final Commit: Studying the Evolution of Multi-CI Service Adoption
Bibliographic record
Abstract
Continuous Integration (CI) services, such as GitHub Actions and Travis CI, are widely adopted in open-source development to automate testing and deployment. Though much existing research often examines individual CI services in isolation, it remains unclear how such multiple services are co-adopted and maintained in practice. To understand how CI adoption is evolving across services, we conducted an empirical study analyzing historical CI adoption of 1 8, 9 2 4 Java projects hosted on GitHub from January 2008 to December 2024, adopting at least one of eight CI services, namely Travis CI, AppVeyor, CircleCI, Azure Pipelines, GitHub Actions, Bitbucket, GitLab CI, and Cirrus CI. Specifically, we investigate: (1) how frequently CI services are co-adopted or replaced, and (2) how maintenance activity varies across different services. Our analysis shows that the use of multiple CI services within the same project is a recurring pattern observed in nearly one in five projects, often reflecting migration across CI services. Our study is among the first to examine multi-CI adoption and maintenance in practice, offering new insights for future research and highlighting the need for strategies and tools to support service selection, coordination, and migration in evolving CI environments.
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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 | 0.010 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".