---
pretty_name: Repository learning training dataset
tags:
- code-review
- github-data
- contrastive-learning
- fine-tuning
- semantic-indexing
- multi-modal
- jsonl
- faiss-index
- tree-sitter
license: mit
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- text-classification
- text-retrieval
- feature-extraction
source_datasets:
- github-repositories
annotations_creators:
- machine-generated
- expert-reviewed
---

# Repository Learning Training Dataset

This dataset contains training data extracted from GitHub repositories for training context-aware code review models. The dataset supports three primary machine learning tasks: contrastive learning, fine-tuning, and semantic indexing.

## Dataset Overview

**Purpose**: Enable training of AI models that understand repository-specific code review patterns and provide contextual feedback.

**Source**: GitHub repositories with rich pull request history and review comments.

## Dataset Structure

```
{repository-name}/
├── contrastive/
│   ├── changed_files_001.json      # Files changed together (positive pairs)
│   ├── changed_files_002.json   
│   └── ...
├── fine_tune/
│   ├── pr_reviews_001.jsonl        # Instruction-following format
│   ├── pr_reviews_002.jsonl
│   └── ...
├── index/
│   ├── functions.json              # AST-extracted function metadata
└── manifest.json                   # Processing metadata
```

## Data Components

### 1. Contrastive Learning Data (`/contrastive/`)

**Format**: JSON files containing file groupings for contrastive learning.

**Purpose**: Learn semantic relationships between code files based on change patterns.

**Structure**:
```json
{
  "pr_12345": [
    "src/components/Button.tsx",
    "src/styles/button.css", 
    "tests/Button.test.tsx"
  ],
  "pr_12346": [
    "src/api/user.py",
    "src/models/user.py",
    "tests/test_user.py"
  ]
}
```

**Usage**: Files changed together form positive pairs; files from different PRs form negative pairs for contrastive learning.

### 2. Fine-Tuning Data (`/fine_tune/`)

**Format**: JSONL files with instruction-following examples.

**Purpose**: Adapt language models to repository-specific review patterns and conventions.

**Structure**:
```json
{
  "prompt": "Code diff:\n```diff\n+def calculate_score(user_data):\n+    return sum(user_data.values())\n```\nPrevious comments:\n- alice: Consider input validation\n\nPlease write a code review comment:",
  "completion": "Good addition! I'd suggest adding type hints and handling edge cases where user_data might be empty or contain non-numeric values."
}
```

**Features**:
- Chronological conversation context
- Multi-turn review discussions
- Repository-specific terminology and patterns
- Code diff context with surrounding discussion

### 3. Semantic Index Data (`/index/`)

**Format**: JSON metadata with function definitions and embeddings.

**Purpose**: Enable fast semantic search across repository functions and documentation.

**Structure** (`functions.json`):
```json
[
  {
    "file": "src/utils/parser.py",
    "name": "parse_diff_hunk", 
    "start_line": 45,
    "end_line": 67,
    "code": "def parse_diff_hunk(hunk_text: str) -> DiffHunk:\n    # Function implementation...",
  }
]
```

**Components**:
- **AST Extraction**: Tree-sitter parsers for different programming languages

## Data Generation Pipeline

### Data Statistics

| Repository | PRs | Review Comments | Functions | Languages |
|------------|-----|-----------------|-----------|-----------|
| dotnet/xharness | 100 | 50 | 1500 | C# |
| dotnet/runtime | N/A | N/A | N/A | C#, c, c++ |

## Usage Examples

If you use this dataset, please refer to https://github.com/kotlarmilos/repository-learning