swedev 0.1.0

Software Engineering Agents with Training and Inference Scaling

🚀 SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling

📝 Blog | 🤗 Huggingface | 💻Github

This repository is a comprehensive pipeline for creating developer-oriented datasets from GitHub repositories, including issue tracking, test case generation, and evaluation.

🔄 Pipeline Overview

Step 0: 🛠️ Configuration Setup

Configuration File

The main configuration file is located at conf/config/default.yaml and contains settings for all pipeline stages:

Validating Configuration

To validate your configuration:

python -m swedev.config --validate

Viewing Configuration

To view the current configuration:

python -m swedev.config --print

Overriding Configuration in Command Line

You can override any configuration value when running scripts:

python your_script.py paths.local_repo_dir=/new/path github.tokens=[token1,token2]

Using Configuration in Code

from swedev.config import Config

# Access basic configuration
conda_base = Config.conda_base
github_tokens = Config.github_tokens

# Access stage-specific settings
localizer_model = Config.Localizer.model
description_model = Config.Description.model
testcase_model = Config.Testcase.model
revise_rounds = Config.Testcase.revise_rounds

Environment Variables Fallbacks

Step 1: 📊 Data Collection from GitHub

Set up your configuration in conf/config/default.yaml with GitHub tokens and repository directories before running these commands.

Option 1: Collect Top PyPI Repositories

You need to install chrome driver first. In ubuntu, you can install simply by apt install chromium-chromedriver

python -m swedev.crawl.get_top_pypi \
    --max_repos 100 \
    --output_folder results/packages \
    --num_workers 8 \
    --start_at 0

Option 2: Fetch All PyPI Repositories

python -m swedev.crawl.pypi_crawler \
    --output results/packages/github_urls.jsonl \
    --workers 16

⚠️ Note: Keep concurrency lower to respect GitHub rate limits

Process the repositories

python -m swedev.issues.get_tasks_pipeline \
    --repo_file results/packages/pypi_rankings.jsonl \
    --output_folder results/issues \
    --cutoff_date 20210101 \
    --num_workers 64 \
    --max_pulls 1000

If you enable --do_clone, the script will clone repositories to the directory specified by local_repo_dir in your configuration.

If you encounter persistent 404 - Error messages, manually terminate and combine results

python -m swedev.issues.get_tasks_pipeline \
    --repo_file results/issues/packages/pypi_rankings.jsonl \
    --output_folder results/issues \
    --combine_results

Step 2: 📝 Generate Test Cases

For parallel environments, create a base environment first to avoid Conda concurrent installation issues:

conda create -n swedevbase python=3.11 -y
conda create -n {env_name} --clone swedevbase # For later usage

Before the generation pipeline, you should config your api info at conf/config.yaml

First, generate descriptions:

python -m swedev.testcases.get_descriptions \
    --dataset_file results/issues/all_tasks.jsonl \
    --output_folder results/descriptions \
    --num_workers 16

Then generate test cases:

python -m swedev.testcases.get_testcases \
    --dataset_file results/descriptions/output.jsonl \
    --top_n 5 \
    --output_folder results/testcases/ \
    --num_workers 4

Step 3: 🧪 Evaluate Test Cases

Docker Method

We provide a Dockerfile based on Ubuntu 22.04 that installs all necessary dependencies for evaluation. The image includes comprehensive development tools. If you encounter errors, you can manually install the dependencies in Dockerfile and then use docker commit to save your image.

First, build the Docker image:

# Build the Docker image from the provided Dockerfile
docker build -t swedev-evaluator:latest .

Run the evaluation container:

docker run -d --network host \
  -v /raid:/raid \
  -w /raid/SWE-Dev \
  --restart always \
  swedev-evaluator:latest \
  /raid/swedev/miniforge3/envs/swedev/bin/python -m swedev.testcases.eval_testcases \
  --dataset /raid/SWE-Dev/results/testcases-0218/output.jsonl \
  --output_folder /raid/SWE-Dev/results/evaluation-0218 \
  --num_workers 48

You should use absolute path when mounting directories

Non-Docker Method

python -m swedev.testcases.eval_testcases \
    --dataset results/testcases-0218/output.jsonl \
    --output_folder results/evaluation-0218 \
    --num_workers 32

Step 4: 📈 View Evaluation Results

python -m swedev.testcases.eval_testcases \
    --dataset results/evaluation-0218/evaluated_testcases \
    --show_report

Step 5: 📦 Create Final Dataset

python swebench.utils.formatter \
    --dataset results/trajectory/qwen-45round-v0227.jsonl \
    --output_folder results/swedata \
    --output_name swe-qwen-45round-v0227.jsonl \
    --dataset_type openhands

🙏 Acknowledgements

We thank the following open-source projects for their contributions:

• SWE-bench

• Agentless

• OpenHands

• Nebius