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---
license: cc-by-4.0
tags:
- code
---
# AutoStub Training Dataset
This dataset contains input-output pairs used for training the AutoStub system, which automatically generates symbolic stubs for external functions during symbolic execution.
## Dataset Description
The dataset consists of input-output pairs from Java standard libraries, focusing on primitive types and string operations. It was generated using a stratified sampling approach to ensure diverse inputs across different data types.
### Dataset Structure
The dataset is organized as a single CSV file with the following columns:
- `method_name`: The name of the Java method
- `sample_id`: Unique identifier for each sample (method_name + sample index)
- `input`: Input values provided to the method (as string representation)
- `input_types`: Types of input parameters
- `output`: Output value returned by the method
- `output_type`: Return type of the method
### Method Coverage
The dataset covers methods from the following Java classes:
- `java.lang.Boolean`
- `java.lang.Double`
- `java.lang.Float`
- `java.lang.String`
- `java.lang.Byte`
- `java.lang.Short`
- `java.lang.Character`
- `java.lang.Integer`
- `java.lang.Long`
- `java.lang.Math`
- `java.lang.StrictMath`
### Dataset Creation
The dataset was created by:
1. Identifying suitable methods in Java standard libraries
2. Using reflection to dynamically invoke methods with random inputs
3. Collecting input-output pairs for each method
4. Applying stratified sampling to ensure diverse inputs
## Usage
### Loading the Dataset
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("Felix6326727/AutoStub")
# Loop through the dataset and print the first 5 samples
for i, sample in enumerate(dataset["train"]):
if i >= 5:
break
print(f"Sample {i}:")
print(f"Method Name: {sample['method_name']}")
print(f"Input: {sample['input']}")
print(f"Input Types: {sample['input_types']}")
print(f"Output: {sample['output']}")
print(f"Output Type: {sample['output_type']}")
print()
```
### Applications
This dataset can be used to:
- Train machine learning models to approximate Java method behavior
- Evaluate the accuracy of symbolic stubs for symbolic execution
- Study the behavior of Java standard library methods across diverse inputs
## Citation
If you use this dataset, please cite:
```bibtex
@inproceedings{maechtle2025autostub,
title={AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution},
author={Felix M{"a}chtle, Nils Loose, Jan-Niclas Serr, Jonas Sander, Thomas Eisenbarth},
booktitle={Proceedings of the 18th ACM/IEEE International Workshop on Search-Based and Fuzz Testing, SBFT 2025},
year={2025}
}
```
## Source
This dataset is derived from the [AutoStub](https://github.com/UzL-ITS/AutoStub) project. |