metadata

license: mit
task_categories:
  - text-classification
  - feature-extraction
language:
  - en
tags:
  - software-engineering
  - testing
  - performance
  - llm-serving
  - vllm
  - benchmarking
  - ml-evaluation
pretty_name: vLLM PR Test Classification
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*

vLLM PR Test Classification Dataset

🎯 Overview

This dataset contains 98 vLLM project commits with their corresponding Pull Request (PR) timeline data and comprehensive test type classifications. It provides insights into testing patterns in a major LLM serving infrastructure project.

📊 Dataset Description

Purpose

This dataset was created by analyzing vLLM project PR timelines to:

Identify different types of testing and benchmarking activities
Understand testing patterns in LLM infrastructure development
Provide labeled data for ML models to classify test types in software PRs
Enable research on performance optimization trends in LLM serving

Test Categories

Each commit is classified across four test categories:

Category	Description	Keywords	Prevalence
LM Evaluation	Language model evaluation tests	`lm_eval`, `gsm8k`, `mmlu`, `hellaswag`, `truthfulqa`	25.5%
Performance	Performance benchmarking tests	`TTFT`, `throughput`, `latency`, `ITL`, `TPOT`, `tok/s`	81.6%
Serving	Serving functionality tests	`vllm serve`, `API server`, `frontend`, `online serving`	53.1%
General Test	General testing activities	`CI`, `pytest`, `unittest`, `buildkite`, `fastcheck`	96.9%

📈 Dataset Statistics

Overall Distribution

Total commits: 98
Multi-category commits: 76 (77.6%)
Average test types per commit: 2.57

Detailed Keyword Frequency

Top Performance Keywords (80 commits)

throughput: 241 mentions
latency: 191 mentions
profiling: 114 mentions
TTFT (Time To First Token): 114 mentions
ITL (Inter-token Latency): 114 mentions
TPOT (Time Per Output Token): 108 mentions
optimization: 87 mentions
tok/s (tokens per second): 66 mentions

Top LM Evaluation Keywords (25 commits)

gsm8k: 62 mentions
lm_eval: 33 mentions
lm-eval: 9 mentions
mmlu: 3 mentions
humaneval: 1 mention

Top Serving Keywords (52 commits)

frontend: 181 mentions
serving: 74 mentions
api server: 42 mentions
vllm serve: 23 mentions
online serving: 19 mentions

🗂️ Data Schema

{
    'commit_hash': str,           # Git commit SHA-1 hash (40 chars)
    'pr_url': str,                # GitHub PR URL (e.g., https://github.com/vllm-project/vllm/pull/12601)
    'has_lm_eval': bool,          # True if commit contains LM evaluation tests
    'has_performance': bool,       # True if commit contains performance benchmarks
    'has_serving': bool,          # True if commit contains serving tests
    'has_general_test': bool,     # True if commit contains general tests
    'test_details': str,          # Pipe-separated test keywords (e.g., "PERF: ttft, throughput | TEST: ci, pytest")
    'timeline_text': str,         # Full PR timeline text with comments, reviews, and commit messages
    'extracted_at': str           # ISO timestamp when data was extracted
}

💻 Usage Examples

Basic Loading

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-username/vllm-pr-test-classification")

# Explore the data
print(f"Total examples: {len(dataset['train'])}")
print(f"Features: {dataset['train'].features}")
print(f"First example: {dataset['train'][0]}")

Filtering Examples

# Get commits with performance benchmarks
perf_commits = dataset['train'].filter(lambda x: x['has_performance'])
print(f"Performance commits: {len(perf_commits)}")

# Get commits with LM evaluation
lm_eval_commits = dataset['train'].filter(lambda x: x['has_lm_eval'])
print(f"LM evaluation commits: {len(lm_eval_commits)}")

# Get commits with multiple test types
multi_test = dataset['train'].filter(
    lambda x: sum([x['has_lm_eval'], x['has_performance'], 
                   x['has_serving'], x['has_general_test']]) >= 3
)
print(f"Commits with 3+ test types: {len(multi_test)}")

Analysis Example

import pandas as pd

# Convert to pandas for analysis
df = dataset['train'].to_pandas()

# Analyze test type combinations
test_combinations = df[['has_lm_eval', 'has_performance', 'has_serving', 'has_general_test']]
combination_counts = test_combinations.value_counts()
print("Most common test combinations:")
print(combination_counts.head())

# Extract performance metrics mentioned
perf_df = df[df['has_performance']]
print(f"\nCommits mentioning specific metrics:")
print(f"TTFT mentions: {perf_df['test_details'].str.contains('TTFT').sum()}")
print(f"Throughput mentions: {perf_df['test_details'].str.contains('throughput', case=False).sum()}")

Text Classification Training

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer

# Prepare for multi-label classification
def preprocess_function(examples):
    # Create multi-label targets
    labels = []
    for i in range(len(examples['commit_hash'])):
        label = [
            int(examples['has_lm_eval'][i]),
            int(examples['has_performance'][i]),
            int(examples['has_serving'][i]),
            int(examples['has_general_test'][i])
        ]
        labels.append(label)
    
    # Tokenize timeline text
    tokenized = tokenizer(
        examples['timeline_text'],
        truncation=True,
        padding='max_length',
        max_length=512
    )
    tokenized['labels'] = labels
    return tokenized

# Train a classifier to identify test types from PR text
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained(
    "bert-base-uncased",
    num_labels=4,
    problem_type="multi_label_classification"
)

🔍 Sample Data

Example 1: Performance-focused commit

{
  "commit_hash": "fc542144c4477ffec1d3de6fa43e54f8fb5351e8",
  "pr_url": "https://github.com/vllm-project/vllm/pull/12563",
  "has_lm_eval": false,
  "has_performance": true,
  "has_serving": false,
  "has_general_test": true,
  "test_details": "PERF: tok/s, optimization | TEST: CI",
  "timeline_text": "[Guided decoding performance optimization]..."
}

Example 2: Comprehensive testing commit

{
  "commit_hash": "aea94362c9bdd08ed2b346701bdc09d278e85f66",
  "pr_url": "https://github.com/vllm-project/vllm/pull/12287",
  "has_lm_eval": true,
  "has_performance": true,
  "has_serving": true,
  "has_general_test": true,
  "test_details": "LM_EVAL: lm_eval, gsm8k | PERF: TTFT, ITL | SERVING: vllm serve | TEST: test, CI",
  "timeline_text": "[Frontend][V1] Online serving performance improvements..."
}

🛠️ Potential Use Cases

Test Type Classification: Train models to automatically classify test types in software PRs
Testing Pattern Analysis: Study how different test types correlate in infrastructure projects
Performance Optimization Research: Analyze performance testing trends in LLM serving systems
CI/CD Insights: Understand continuous integration patterns in ML infrastructure projects
Documentation Generation: Generate test documentation from PR timelines
Code Review Automation: Build tools to automatically suggest relevant tests based on PR content

📚 Source

This dataset was extracted from the vLLM project GitHub repository PR timelines. vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.

🔄 Updates

v1.0.0 (2025-01): Initial release with 98 commits

📜 License

This dataset is released under the MIT License, consistent with the vLLM project's licensing.

📖 Citation

If you use this dataset in your research or applications, please cite:

@dataset{vllm_pr_test_classification_2025,
  title={vLLM PR Test Classification Dataset},
  author={vLLM Community Contributors},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/your-username/vllm-pr-test-classification},
  note={A dataset of 98 vLLM commits with test type classifications extracted from GitHub PR timelines}
}

🤝 Contributing

If you'd like to contribute to this dataset or report issues:

Open an issue on the Hugging Face dataset repository
Submit improvements via pull requests
Share your use cases and findings

⚠️ Limitations

Dataset size is limited to 98 commits
Timeline text may be truncated for very long PR discussions
Classification is based on keyword matching, which may miss context-dependent references
Dataset represents a snapshot from specific time period of vLLM development

🙏 Acknowledgments

Thanks to the vLLM project maintainers and contributors for their open-source work that made this dataset possible.