datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CodeReviewWithSummaryQAcodereviewerCodeReviewQA CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
The task of automated code refinement aims to automate the developer's perspective in resolving an actionable code review comment provided by a reviewer.
This is a generative task, where the LLM is required to revise a pre-review code submission with respect to the natural language code review comment to produce an intended post-review code revision.
CodeReviewQA further breaks down this generative task… See the full description on the dataset page: https://huggingface.co/datasets/Tomo-Melb/CodeReviewQA.code-reviewA Scrape of the codereview stack exchange, good for high quality code
code-review-dataset-ja
Japanese Code Review Dataset (500 Samples)
A dataset of 500 code review pairs (buggy code + fixed code) with Japanese review comments. Designed for training and evaluating code review assistance models.
Total samples: 500
Languages: Python (220), JavaScript (136), Go (54), Rust (52), TypeScript (38)
Difficulties: Easy (135), Medium (259), Hard (106)
Bug types: Logic Error (120), Null Pointer (81), Off-by-One (80), Edge Case (77), Type Error (60), Security (42), Performance (40)… See the full description on the dataset page: https://huggingface.co/datasets/gram-chan-jp/code-review-dataset-ja.code-review-findings-samples
Code Review Findings Samples
Curated synthetic examples for evaluating automated code review pipelines — especially the
AI Code Reviewer MCP stack built with
Qwen3.6-27B.
Each row contains a short code snippet, the analysis type, and a structured JSON output that
matches the review contract used by ImTamsi/qwen3.6-27b-code-reviewer.
Dataset structure
Column
Description
id
Stable sample identifier
analysis_type
review, bugs, security, performance… See the full description on the dataset page: https://huggingface.co/datasets/ImTamsi/code-review-findings-samples.code-review-dpo-3k
Code Review DPO Pairs (3K)
DPO preference pairs for training LLMs to produce specific, actionable, educational code reviews.
Dataset Description
3,000 preference pairs across 4 programming languages:
Language
Examples
Python
~64%
JavaScript
~12%
TypeScript
~12%
Go
~12%
8 review scenarios covering real-world code quality issues:
SQL injection & security vulnerabilities
XSS via innerHTML
Hardcoded credentials
Resource leaks (unclosed… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/code-review-dpo-3k.