FLOWSTEP Abstracted Full Test - Type Accuracy: leaderboard
FLOWSTEP v1 evaluates next-step prediction for GitHub Actions. It uses a repository-level 8:1:1 split and 13630 full test steps. Abs and Raw are distinct abstraction conditions. Generation uses temperature 0 and one candidate; calibrated script-model selection uses 1000 random test Script instances. The full pipeline combines RoBERTa type/action heads with Sonnet 4.5 one-shot prompting or a trained T5-small; the weights are trained on distinct task sets. Judge validation uses 350 incorrect scripts, 2 human annotators, and 3 ordinal functional classes. Signed agreement and correlation measure judgment quality, not generation accuracy. The two FLOWSTEP pipelines share these exact RoBERTa component predictions, which are included only once. Metric: Type Accuracy (%).
Metric: Type Accuracy (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 2 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | FLOWSTEP trained RoBERTa Type Accuracy | 92.9 | |
| 2 | FLOWSTEP study GH-WCOM | 82.86 |
Interactive version: theaggregate.ai/benchmark?slug=flowstep-abstracted-full-test-type-accuracy · How It Works · Data refreshed daily, snapshot 2026-10-28.