RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction
Bibliographic record
Abstract
Continuous Integration (CI) is a common practice adopted by modern software organizations. It plays an especially important role for large corporations like Ubisoft, where thousands of build jobs are submitted daily. Indeed, the cadence of development progress is constrained by the pace at which CI services process build jobs. To provide faster CI feedback, recent work explores how build outcomes can be anticipated. Although early results show plenty of promise, the distinct characteristics of Project X—a AAA video game project at Ubisoft—present new challenges for build outcome prediction. In the Project X setting, changes that do not modify source code also incur build failures. We also observe that the code changes that have an impact that crosses the source-data boundary are more prone to build failures than code changes that do not impact data files. Since such changes are not fully characterized by the existing set of features for build outcome prediction, state-of-the-art models tend to underperform. To incorporate the data context, we propose RavenBuild—a novel approach to build outcome prediction that leverages context-, relevance-, and dependency-aware features. In the Project X context, we observe that RavenBuild improves the F1-score of the failing class by 50%, the recall of the failing class by 105%, and the AUC by 11% with respect to the state-of-the-art BuildFast approach. To ease adoption in settings with heterogeneous project sets, we also provide a simplified alternative RavenBuild-CR, which excludes dependency-aware features. We observe across-the-board improvements when RavenBuild-CR is applied to 22 open-source projects and Project X. On the other hand, we find that a naïve Parrot approach, which simply echoes the previous build outcome as its prediction, is surprisingly competitive with BuildFast and RavenBuild. Though Parrot fails to predict when the build outcome differs from their immediate predecessor, Parrot serves well as a tendency indicator of the sequences in build outcome datasets. Thus, we recommend that future studies also compare to the Parrot approach as a baseline when evaluating build outcome prediction models.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".