Datasets:

Felix6326727
/

AutoStub

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:

Identifying suitable methods in Java standard libraries
Using reflection to dynamically invoke methods with random inputs
Collecting input-output pairs for each method
Applying stratified sampling to ensure diverse inputs

Usage

Loading the Dataset

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:

@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 project.