update readme

README.md

@@ -7,10 +7,11 @@ pretty_name: JunoBench
 
 # 📘 JunoBench: A Benchmark Dataset of Crashes in Machine Learning Python Jupyter Notebooks
 
+This repository is the compiled benchmark dataset of paper "JunoBench: A Benchmark Dataset of Crashes in Machine Learning Python Jupyter Notebooks". The code repository of constructing the benchmark is on GitHub: [JunoBench_construct](https://github.com/PELAB-LiU/JunoBench_construct). This dataset is suitable for studying crashes (e.g., bug reproducing, detection, diagnose, localization, and repair) in ML notebooks.
 
 ## 📂 Contents
 
-The benchmark dataset contains 111 reproducible ML notebooks with crashes, each paired with a verifiable fix. It covers a broad range of popular ML libraries, including TensorFlow/Keras, PyTorch, Scikit-learn, Pandas, and NumPy, as well as notebook-specific out-of-order execution issue. To support reproducibility and ease of use, JunoBench offers a unified execution environment in which all crashes and fixes can be reliably reproduced.
+This benchmark dataset contains 111 reproducible ML notebooks with crashes, each paired with a verifiable fix. It covers a broad range of popular ML libraries, including TensorFlow/Keras, PyTorch, Scikit-learn, Pandas, and NumPy, as well as notebook-specific out-of-order execution issue. To support reproducibility and ease of use, JunoBench offers a unified execution environment in which all crashes and fixes can be reliably reproduced.
 
 The ML library distribution is as follows:
 
@@ -33,51 +34,20 @@ The structure of the benchmark repository is the following:
 
 ## 🛠 Usage
 
-This dataset is suitable for studying crashes (e.g., bug reproducing, detection, localization, and repair) in ML notebooks.
+### 📄 Environment: using our shared Docker image
 
-You can use the requirements specification or Docker to recreate the exact environments.
-
-### 📄 Reproducing Environment Option 1 (recommended): using our shared Docker image
-
-To ensure full reproducibility (including Python version, system libraries, and all dependencies), you can use our compiled docker image directly:
+We compiled a docker image to ensure full reproducibility (including Python version, system libraries, and all dependencies):
 
 ```
 docker pull yarinamomo/kaggle_python_env:latest
 ```
 
-Then run the image:
+To run the image:
 
 ```
 docker run -v [volumn mount windows path]:/junobench_env -w=/junobench_env -p 8888:8888 -it yarinamomo/kaggle_python_env:latest jupyter notebook --no-browser --ip="0.0.0.0" --notebook-dir=/junobench_env --allow-root
 ```
 
-### 📄 Reproducing Environment Option 2: build your own Docker image
-
-You can also choose to build your own docker image by using our pre-configured Docker image from this repository: 
-
-📦 GitHub Repo: [yarinamomo/docker-kaggle-python](https://github.com/yarinamomo/docker-kaggle-python)
-
-### 📄 Reproducing Environment - Option 3: using virtual envrironment (not recommended)
-
-You can recreate the Python environment on your system as follows:
-
-#### Requirements
-- Python 3.10
-- `pip`, `venv`, and basic build tools
-- [requirements.txt](./requirements.txt)
-
-#### Setup Instructions
-
-**Linux** or **WSL** on Windows (the same requirements file as in the Docker image):
-```bash
-# Create a virtual environment
-python3.10 -m venv venv
-source venv/bin/activate
-
-# Install required packages
-pip install -r requirements.txt
-```
-
 ### 🐳 Execute notebooks with `auto_notebook_executer.py`:
 
 You can execute the notebooks by using this tool. This tool can be used by command line:
@@ -94,12 +64,6 @@ You can also execute the notebooks via a Jupyter notebook platform. The executio
 
 For notebook specific errors (NBspecific_1-20), there maybe due to one cell being executed more than once. For those cases, the cells with an identifier "[reexecute]" or "[re-execute]" should be executed twice consecutively.
 
-## 📖 Citation
-
-If you use this dataset in your research, please cite:
-
-[todo]
-
 ## 📝 License & Attribution
 
 This project is for academic research purposes only.