---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - code-review
  - software-engineering
  - swe-bench
  - agentic
  - trajectories
  - sft
pretty_name: SWE-Review-Traj
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: pr_instance_id
      dtype: large_string
    - name: issue_instance_id
      dtype: large_string
    - name: repo
      dtype: large_string
    - name: version
      dtype: large_string
    - name: base_commit
      dtype: large_string
    - name: created_at
      dtype: large_string
    - name: image_name
      dtype: large_string
    - name: problem_statement
      dtype: large_string
    - name: difficulty
      dtype: int64
    - name: patchgen_model
      dtype: large_string
    - name: model_patch
      dtype: large_string
    - name: patch_resolved
      dtype: bool
    - name: pr_title
      dtype: large_string
    - name: pr_body
      dtype: large_string
    - name: messages
      dtype: large_string
    - name: tools
      dtype: large_string
    - name: turns
      dtype: int64
    - name: teacher_model
      dtype: large_string
    - name: decision
      dtype: large_string
    - name: confidence
      dtype: float64
    - name: report_json
      dtype: large_string
    - name: judge_gpt54
      dtype: large_string
    - name: judge_opus46
      dtype: large_string
  splits:
    - name: train
      num_examples: 8914
---

# SWE-Review-Traj

<p align="center">
<img src="assets/pipeline_overview.png" width="100%"/>
</p>


**8,914 decision-correct agentic code review trajectories** for training open-source code review models. Each trajectory captures a full multi-turn review session where an AI agent explores a repository, traces the root cause of a bug, and produces a structured review decision — all verified against executable test suites.

[**Project Page**](https://swe-lego.github.io/SWE-Review/) | [**Paper**](https://arxiv.org/abs/2607.06065) | [**Code**](https://github.com/SWE-Lego/SWE-Review) | [**Benchmark**](https://huggingface.co/datasets/SWE-Lego/SWE-Review-Bench) | [**Training Data**](https://huggingface.co/datasets/SWE-Lego/SWE-Review-Traj) | [**Claude Code Plugin**](https://github.com/SWE-Lego/cc-swe-review)

## About SWE-Review

**SWE-Review** is a framework for closing the issue-resolution loop with agentic code review.
A reviewer agent independently explores the repository, traces the root cause, and compares its own diagnosis against the submitted PR — turning one-shot patch generation into an iterative generate-review-revise loop that raises resolve rates by up to +29.4 percentage points on SWE-bench Verified.

## Data Collection Pipeline

```
SWE-rebench (~6K instances) → Patchgen (3 models) → 14,156 candidate PRs
  → Teacher Review (GLM-5, agentic) → Decision-Correct Filtering → 8,914 trajectories
```

### Step 1: Candidate PR Generation

Three models generate patches for ~6,000 SWE-rebench instances (**no overlap** with SWE-Review-Bench to prevent data leakage):

| Patchgen Model | Trajectories |
|----------------|-------------|
| GLM-5-FP8 | 3,056 |
| Qwen3-Coder-30B-A3B-Instruct | 2,991 |
| Qwen3-30B-A3B-Instruct-2507 | 2,867 |

### Step 2: Teacher Review

Open-weight **GLM-5 with thinking enabled** reviews each PR agentically via OpenHands-SDK. The review prompt enforces **independent reconstruction**: the reviewer must trace the root cause from the issue description and repository *before* examining the candidate patch, preventing confirmation bias.

### Step 3: Decision-Correct Filtering

Only trajectories where the reviewer's decision matches ground truth are retained:
- **Approve** a patch that actually resolves the issue ✓
- **Request changes** on a patch that does not resolve the issue ✓

### Quality Validation

- **Semantic quality**: Two independent judges (Claude Opus 4.6, GPT-5.4) rate diagnosis quality. Mean overall score >3.0/5. Cohen's κ = 0.72 (substantial inter-judge agreement).
- **Functional quality**: Diagnoses carry actionable information beyond the binary decision — revision RRR rises from 3% (no review) → 8% (decision only) → **21% (teacher review)** → 32% (oracle).

## Training Value

Review trajectories are not only useful for training reviewers — they also improve code generation when mixed with issue-resolution data:

| Training Data (Qwen3-8B) | RR (one-shot fix) | Loop RRR (self-review + revise) |
|---------------------------|------|------|
| Issue-resolution 2k | 31.2% | 31.2% |
| **+ Review 2k** | **36.8%** (+5.6pp) | **41.8%** (+10.6pp) |
| Issue-resolution 3k | 34.0% | 34.0% |
| **+ Review 3k** | **37.8%** (+3.8pp) | **41.2%** (+7.2pp) |

Mixed training simultaneously improves one-shot resolve rate and enables self-contained generate-review-revise loops within a single model.

## Usage

### Load the Dataset

```python
from datasets import load_dataset
import json

ds = load_dataset("SWE-Lego/SWE-Review-Traj", split="train")
print(f"Total trajectories: {len(ds)}")

# Inspect a trajectory
traj = ds[0]
print(f"Instance: {traj['pr_instance_id']}")
print(f"Decision: {traj['decision']} (confidence: {traj['confidence']:.2f})")
print(f"Turns: {traj['turns']}")
print(f"Patch resolved: {traj['patch_resolved']}")
```

### Train with LLaMA-Factory

The `messages` field is in **ShareGPT format** — directly compatible with LLaMA-Factory:

```python
import json

ds = load_dataset("SWE-Lego/SWE-Review-Traj", split="train")
sft_data = [json.loads(row["messages"]) for row in ds]

with open("swe_review_traj.json", "w") as f:
    json.dump(sft_data, f)
```

Then register in `dataset_info.json` and train with LLaMA-Factory. See the [training guide](https://github.com/SWE-Lego/SWE-Review/tree/main/scripts/train) for complete instructions.

### Access Judge Scores

4,548 trajectories include dual-judge quality scores:

```python
traj = ds[0]
if traj["judge_gpt54"]:
    scores = json.loads(traj["judge_gpt54"])
    print(f"Diagnosis accuracy: {scores['diagnosis_accuracy']}/5")
    print(f"Suggestion correctness: {scores['suggestion_correctness']}/5")
    print(f"Overall: {scores['overall_score']}/5")
```

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `pr_instance_id` | string | PR instance identifier |
| `issue_instance_id` | string | Issue instance identifier |
| `repo` | string | GitHub repository |
| `problem_statement` | string | GitHub issue description |
| `patchgen_model` | string | Model that generated the candidate PR |
| `model_patch` | string | Candidate patch (unified diff) |
| `patch_resolved` | bool | Whether the patch resolves the issue |
| `messages` | string (JSON) | Full conversation in ShareGPT format |
| `tools` | string (JSON) | Tool definitions used during review |
| `turns` | int | Number of conversation turns |
| `teacher_model` | string | Teacher model used for review |
| `decision` | string | `approve` or `request_changes` |
| `confidence` | float | Reviewer confidence (0.0–1.0) |
| `report_json` | string (JSON) | Structured review report |
| `judge_gpt54` | string (JSON) | GPT-5.4 quality scores (null if not evaluated) |
| `judge_opus46` | string (JSON) | Claude Opus 4.6 quality scores (null if not evaluated) |

### Judge Score Fields

Each judge JSON contains:

| Field | Range | Description |
|-------|-------|-------------|
| `diagnosis_accuracy` | 1–5 | Correctness of root-cause identification |
| `suggestion_correctness` | 1–5 | Correctness of suggested fixes |
| `grounding_quality` | 1–5 | Whether suggestions reference specific code locations |
| `overall_score` | 1–5 | Overall review quality |
| `verdict` | 1–5 | Alignment with ground-truth resolution status |

## Citation

```bibtex
@misc{wang2026swereview,
  title={SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review},
  author={Ruoyu Wang and Jierun Chen and Shaowei Wang and Chaofan Tao and Sidi Yang and Yuxin Jiang and Kim-Hui Yap and Lifeng Shang and Xiaohui Li and Haoli Bai},
  year={2026},
  eprint={2607.06065},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2607.06065}
}
```