Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have far-reaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network's development team. To tackle this challenge, we present Socialz, a novel approach to social fuzz testing that (1) characterises real users of a social network, (2) diversifies their interaction using evolutionary computation across multiple, non-trivial features, and (3) collects performance data as these interactions are executed. With Socialz, we aim to provide anyone with the capability to perform comprehensive social testing, thereby improving the reliability and security of online social networks used around the world.

翻译：在线社交网络已成为我们日常生活的组成部分，并在塑造人际关系中发挥关键作用。然而，即使是微小的错误或故障，也可能导致从令人困扰的问题到严重的数据泄露，对数百万用户产生深远影响。为降低这些风险，模糊测试（一种基于随机输入的测试方法）能够提升社交网络功能正确性的置信度。然而，对于网络开发团队之外的程序员而言，实施传统模糊测试方法可能极为困难或不切实际。为解决这一挑战，我们提出了Socialz——一种社交模糊测试的新方法，该方法能够（1）对社交网络真实用户进行特征化建模，（2）通过跨多个非平凡特征的进化计算实现用户交互多样化，（3）在交互执行过程中收集性能数据。借助Socialz，我们致力于为所有人提供执行全面社交测试的能力，从而提升全球在线社交网络的可靠性与安全性。