Quick Presets:
Highest Precision ↗
High Risk + File Context (Precision) ↗
Best for Complex Code (Precision) ↗
Python + Medium Risk (Precision) ↗
Best for Concurrency (Precision) ↗
Typescript + Scheduling (Recall) ↗
Best for Performance Optimization ↗
Best for Bug Fixes (Recall) ↗
Best for Small Go PRs ↗
Java + Authentication ↗
Small PRs + Performance Optimization (Precision) ↗
Best for Medium Ruby PRs ↗
Best for Bug Fixes ↗
Typescript + Correctness ↗
Ruby + Medium PRs (Recall) ↗
Best for Ui ↗
Best for Reliability ↗
Best for Concurrency ↗
Ruby + Correctness ↗
Best for Caching ↗
Best for Go ↗
Best for Small PRs ↗
Best for Scheduling ↗
Best for File Context ↗
Best for Security ↗
Security Critical ↗
Best for High Risk ↗
Best for Python ↗
Best for Medium Python PRs ↗
Best for Moderate Bugs ↗
Highest Recall ↗
Best for Authentication ↗
High Risk Auth ↗
Best for Critical Risk ↗
Best for Medium Java PRs ↗
Best for Features ↗
Best for Moderate Code ↗
Best for Complex Code ↗
Complex & Subtle ↗
Best for Correctness ↗
Best for Java ↗
Best for Large PRs ↗
Highest F1 ↗
Best for Typescript ↗
Best for Subtle Bugs ↗
Best for Cross-File ↗
Best for Medium PRs ↗
Best for Medium Risk ↗
Best for Reliability ↗
Best for Concurrency ↗
Best for Ruby ↗
CURRENT RESULTS
All Languages
Performance Metrics
# Tool Precision (%) Recall (%) F1 Score (%) True Positives PRs Evaluated
F1 Score by Tool

Repositories Used

The offline benchmark draws from a diverse set of open-source repositories spanning different languages, frameworks, and domains — from infrastructure and observability tools to web platforms and security projects.

This variety ensures our results reflect how AI reviewers perform across real-world codebases, not just one type of software.