Forecasting the Duration of Incremental Build Jobs
Bibliographic record
Abstract
Build systems automate the process of compiling, testing, packaging, and deploying modern software systems. While building a simple program may only take a few seconds on most modern computers, it may take hours, if not days, to build large software systems. Since modern build tools do not provide estimates of how long a build job will take, development and release teams cannot plan human and computer resources optimally. To fill this gap, we propose BuildMétéo-a tool to forecast the duration of incremental build jobs. BuildMétéo analyzes a timing-annotated Build Dependency Graph (BDG) that we extract from the build system to forecast build job duration. We evaluate BuildMétéo by comparing forecasts to the timed execution of 2,163 incremental build jobs derived from replayed commits of the GLIB and VTK open source systems.We find that: (a) 87% of the studied commits do not change the BDG, suggesting that reasoning about build job duration using the BDG is a sensible starting point; (b) 94% of incremental build jobs that do not change the BDG have an estimation error of under ten seconds; and (c) build jobs with larger sets of modified files tend to yield more accurate duration forecasts. These results suggest that BuildMétéo can improve the transparency of build jobs, and thus, aid practitioners in build-related decision making.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".