Reuse (or Lack Thereof) in Travis CI Specifications: An Empirical Study of CI Phases and Commands
Bibliographic record
Abstract
Continuous Integration (CI) is a widely used practice where code changes are automatically built and tested to check for regression as they appear in the Version Control System (VCS). CI services allow users to customize phases, which define the sequential steps of build jobs that are triggered by changes to the project. While past work has made important observations about the adoption and usage of CI, little is known about patterns of reuse in CI specifications. Should reuse be common in CI specifications, we envision that a tool could guide developers through the generation of CI specifications by offering suggestions based on popular sequences of phases and commands. To assess the feasibility of such a tool, we perform an empirical analysis of the use of different phases and commands in a curated sample of 913 CI specifications for Java-based projects that use Travis CI-one of the most popular public CI service providers. First, we observe that five of nine phases are used in 18%-75% of the projects. Second, for the five most popular phases, we apply association rule mining to discover frequent phase, command, and command category usage patterns. Unfortunately, we observe that the association rules lack sufficient support, confidence, or lift values to be considered statistically significantly interesting. Our findings suggest that the usage of phases and commands in Travis CI specifications are broad and diverse. Hence, we cannot provide suggestions for Java-based projects as we had envisioned.
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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 | 0.020 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| 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".