How Trustworthy Is Your Continuous Integration (CI) Accelerator?: A Comparison of the Trustworthiness of CI Acceleration Products
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
The practice of Continuous Integration (CI) allows developers to quickly integrate and verify projects modifications. Thus, CI acceleration products are a boon to developers seeking rapid feedback. However, if outcomes vary between accelerated and non-accelerated settings, the trustworthiness of the acceleration is called into question. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this paper, we study the trustworthiness of two CI acceleration products, one based on program analysis (PA) and the other on machine learning (ML). We re-execute 50 failing builds from ten open-source projects in non-accelerated (baseline), PAaccelerated, and ML-accelerated settings. We find that when applied to known failing builds, PA-accelerated builds more often (43.83 percentage point difference across ten projects) align with the non-accelerated build results. We conclude that while there is still room for improvement for both CI acceleration products, the selected PA-product currently provides a more trustworthy signal of build outcomes than the ML-product.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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