安全的多方测试案例数据生成通过生成的对抗网络
Zheng Wang1, Liutao Zhao2, Fanyin Meng3
1Institute of Digital Economy, Beijing Academy of Science and Technology, Beijing, 100032, China.
Scientific reports
|January 12, 2026
概括
本研究介绍了一种保护隐私的生成对抗网络 (GAN),用于联合测试案例数据生成. 该方法在分布式环境中提高了软件测试效率和数据质量.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 软件测试案例数据生成的挑战包括质量差和合成困难.
- 跨组织的分布式数据和隐私问题阻碍了集中式数据聚合.
- 联合学习环境对于安全的,跨组织的数据处理是必要的.
研究的目的:
- 提出一种基于生成对抗网络 (GAN) 的方法,用于保护隐私,联合测试案例数据生成.
- 解决分布式软件测试中的数据质量,合成和隐私挑战.
主要方法:
- 一种基于GAN的方法,使用协议语法深度学习框架.
- 测试案例编码器-解码器机制和GAN驱动的样本字符生成器.
- 发电机和区分器的本地培训,在联合环境中安全聚合模型参数.
主要成果:
- 拟议的方法产生高质量,多样化的测试案例数据,同时保持隐私.
- 与传统方法相比,生成的数据显示了更高的覆盖率和有效性.
- 显著提高软件测试的效率和质量.
结论:
- 该框架提供了一个可扩展的解决方案,用于在联合环境中生成测试数据.
- 在跨组织环境中有效解决数据主权要求.
- 帮助识别关键基础设施中的潜在漏洞.
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