README.md

---
dataset_info:
- config_name: vllm
  features:
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  - name: pr_url
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  - name: pr_date
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  - name: analysis_extracted_at
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  - name: stats
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  - name: diff_text
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    num_examples: 39
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- config_name: sglang
  features:
  - name: commit_hash
    dtype: string
  - name: pr_url
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  - name: pr_date
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  - name: has_throughput
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  - name: uses_lm_eval
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  - name: commit_message
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configs:
- config_name: vllm
  default: true
  data_files:
  - split: train
    path: vllm/train-*
- config_name: sglang
  data_files:
  - split: train
    path: sglang/train-*
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
tags:
- performance-optimization
- software-engineering
- benchmark
- ai-agents
- code
---

# ISO-Bench Dataset

A curated dataset of real-world software performance optimization commits from [vLLM](https://github.com/vllm-project/vllm) and [SGLang](https://github.com/sgl-project/sglang), designed for evaluating AI agents on code optimization tasks.

## Dataset Summary

| Config | Commits | Repository |
|--------|---------|------------|
| `vllm` | 39 | vLLM (LLM inference engine) |
| `sglang` | 15 | SGLang (LLM serving framework) |

Each entry represents a human-authored performance optimization commit with:
- The original commit diff and message
- Performance benchmark commands (`perf_command`)
- Model configurations for benchmarking
- Hardware requirements
- API surface analysis

## Usage

```python
from datasets import load_dataset

# Load vLLM optimization commits
vllm = load_dataset('Lossfunk/ISO-Bench', 'vllm', split='train')

# Load SGLang optimization commits
sglang = load_dataset('Lossfunk/ISO-Bench', 'sglang', split='train')

# Example: inspect a commit
print(vllm[0]['commit_subject'])
print(vllm[0]['perf_command'])
print(vllm[0]['models'])
```

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `commit_hash` | string | Short hash of the optimization commit |
| `pr_url` | string | URL to the pull request |
| `commit_subject` | string | Commit message subject line |
| `commit_message` | string | Full commit message |
| `diff_text` | string | Unified diff of the optimization |
| `models` | list[string] | HuggingFace model IDs used for benchmarking |
| `perf_command` | string | Command to run the performance benchmark |
| `has_serving` | bool | Whether commit affects serving performance |
| `has_latency` | bool | Whether commit affects latency |
| `has_throughput` | bool | Whether commit affects throughput |
| `uses_lm_eval` | bool | Whether correctness is validated via lm-eval |
| `lm_eval_command` | string | lm-eval command for correctness validation |
| `files_changed` | list[string] | Files modified in the commit |
| `apis` | list[string] | Affected API endpoints/functions |
| `affected_paths` | list[string] | Code paths affected by the change |
| `hardware` | string | Required hardware (e.g., GPU type) |
| `stats` | struct | Commit statistics (lines changed, files, hunks) |

## How It Works

Each dataset entry captures a real performance optimization made by an expert developer. AI agents are given the codebase at the parent commit (before optimization) and must independently discover and implement a performance improvement. Their patches are then benchmarked against the human expert's solution using wall-clock timing comparisons.

## License

Apache 2.0