Studying the impact of adopting continuous integration on the delivery time of pull requests
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.
Bibliographic record
Abstract
Continuous Integration (CI) is a software development practice that leads developers to integrate their work more frequently. Software projects have broadly adopted CI to ship new releases more frequently and to improve code integration. The adoption of CI is motivated by the allure of delivering new functionalities more quickly. However, there is little empirical evidence to support such a claim. Through the analysis of 162,653 pull requests (PRs) of 87 GitHub projects that are implemented in 5 different programming languages, we empirically investigate the impact of adopting CI on the time to deliver merged PRs. Surprisingly, only 51.3% of the projects deliver merged PRs more quickly after adopting CI. We also observe that the large increase of PR submissions after CI is a key reason as to why projects deliver PRs more slowly after adopting CI. To investigate the factors that are related to the time-to-delivery of merged PRs, we train regression models that obtain sound median R-squares of 0.64-0.67. Finally, a deeper analysis of our models indicates that, before the adoption of CI, the integration-load of the development team, i.e., the number of submitted PRs competing for being merged, is the most impactful metric on the time to deliver merged PRs before CI. Our models also reveal that PRs that are merged more recently in a release cycle experience a slower delivery time.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it