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重要组成部分，在塑造人际关系中扮演着关键角色。然而，即使是微小的程序错误或故障，也可能引发从令人困扰的问题到严重数据泄露等各类事件，对数百万用户产生深远影响。为降低此类风险，模糊测试（一种通过随机输入进行测试的方法）能提升对社交网络正确功能的信心。然而，对网络开发团队以外的程序员而言，传统模糊测试方法的实施可能极为困难或不切实际。为应对这一挑战，我们提出Socialz——一种新型社交模糊测试方法，其核心包括：（1）刻画社交网络的真实用户特征；（2）通过进化计算在多个非平凡特征维度上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，我们旨在使任何人具备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。