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Related Experiment Video

Updated: Jan 14, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Secure multi-party test case data generation through generative adversarial networks.

Zheng Wang¹, Liutao Zhao², Fanyin Meng³

¹Institute of Digital Economy, Beijing Academy of Science and Technology, Beijing, 100032, China.

Scientific Reports

|January 12, 2026

Summary

This summary is machine-generated.

This study introduces a privacy-preserving Generative Adversarial Network (GAN) for federated test case data generation. The method enhances software testing efficiency and data quality in distributed environments.

Keywords:

Autoencoders Federated Learning Generative Adversarial Networks Test Case Generation

Related Experiment Videos

Last Updated: Jan 14, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

Software Engineering
Artificial Intelligence
Data Privacy

Background:

Challenges in software test case data generation include poor quality and synthesis difficulties.
Distributed data across organizations and privacy concerns hinder centralized data aggregation.
Federated learning environments are necessary for secure, cross-organizational data handling.

Purpose of the Study:

To propose a Generative Adversarial Network (GAN)-based method for privacy-preserving, federated test case data generation.
To address data quality, synthesis, and privacy challenges in distributed software testing.

Main Methods:

A GAN-based approach utilizing a protocol grammar deep learning framework.
Test case encoder-decoder mechanisms and a GAN-driven sample character generator.
Local training of generator and discriminator with secure aggregation of model parameters in a federated setting.

Main Results:

The proposed method generates high-quality, diverse test case data while preserving privacy.
Generated data shows superior coverage and effectiveness compared to traditional methods.
Significantly enhances software testing efficiency and quality.

Conclusions:

The framework offers a scalable solution for generating test data in federated environments.
Effectively addresses data sovereignty requirements in cross-organizational settings.
Facilitates the identification of latent vulnerabilities in critical infrastructure.