Use and Misuse of Continuous Integration Features: An Empirical Study of Projects That (Mis)Use Travis CI
Bibliographic record
Abstract
Continuous Integration (CI) is a popular practice where software systems are automatically compiled and tested as changes appear in the version control system of a project. Like other software artifacts, CI specifications require maintenance effort. Although there are several service providers like TRAVIS CI offering various CI features, it is unclear which features are being (mis)used. In this paper, we present a study of feature use and misuse in 9,312 open source systems that use TRAVIS CI. Analysis of the features that are adopted by projects reveals that explicit deployment code is rare-48.16 percent of the studied TRAVIS CI specification code is instead associated with configuring job processing nodes. To analyze feature misuse, we propose HANSEL-an anti-pattern detection tool for TRAVIS CI specifications. We define four anti-patterns and HANSEL detects anti-patterns in the TRAVIS CI specifications of 894 projects in the corpus (9.60 percent), and achieves a recall of 82.76 percent in a sample of 100 projects. Furthermore, we propose GRETEL-an anti-pattern removal tool for TRAVIS CI specifications, which can remove 69.60 percent of the most frequently occurring antipattern automatically. Using GRETEL, we have produced 36 accepted pull requests that remove TRAVIS CI anti-patterns automatically.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".