Do Not Trust Build Results at Face Value - An Empirical Study of 30 Million CPAN Builds
Bibliographic record
Abstract
Continuous Integration (CI) is a cornerstone of modern quality assurance, providing on-demand builds (compilation and tests) of code changes or software releases. Despite the myriad of CI tools and frameworks, the basic activity of interpreting build results is not straightforward, due to not only the number of builds being performed but also, and especially, due to the phenomenon of build inflation, according to which one code change can be built on dozens of different operating systems, run-time environments and hardware architectures. As existing work mostly ignored this inflation, this paper performs a large-scale empirical study of the impact of OS and run-time environment on build failures on 30 million builds of the CPAN ecosystem's CI environment. We observe the evolution of build failures over time, and investigate the impact of OSes and environments on build failures. We show that distributions may fail differently on different OSes and environments and, thus, that the results of CI require careful filtering and selection to identify reliable failure data.
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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 | 0.013 | 0.111 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".