CI/CD Configuration Practices in Open Source Android Apps: An Empirical Study
Bibliographic record
Abstract
Continuous Integration and Continuous Delivery (CI/CD) is a well-established practice that automatically builds, tests, packages, and deploys software systems. To adopt CI/CD, software developers need to configure their projects using dedicated YML configuration files. Mobile apps have distinct characteristics with respect to CI/CD practices, such as testing on various emulators and deploying to app stores. However, little is known about the challenges and added value of adopting CI/CD in mobile apps and how developers maintain such a practice. In this article, we conduct an empirical study on CI/CD practices in \(2{,}557\) Android apps adopting 4 popular CI/CD services, namely GitHub Actions, Travis CI, CircleCI, and GitLab CI/CD. We also compare our findings with those reported in prior research on general CI/CD practices to situate them within broader trends. We observe a lack of commonality and standardization across CI/CD services and Android apps, leading to complex YML configurations and associated maintenance efforts. We also observe that CI/CD configurations focus primarily on the build setup, with around half of the projects performing standard testing and only 9% incorporating deployment. In addition, we find that CI/CD configurations are changed bi-monthly on average, with frequent maintenance correlating with active issue tracking, project size/age, and community engagement. Our qualitative analysis of commits uncovered 11 themes in CI/CD maintenance activities, with over a third of the changes focusing on improving workflows and fixing build issues, whereas another third involves updating the build environment, tools, and dependencies. Our study emphasizes the necessity for automation and AI-powered tools to improve CI/CD processes for mobile apps and advocates creating adaptable open source tools to efficiently manage resources, especially in testing and deployment.
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.
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 | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 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".