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 farreaching 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 social 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 put social testing tools in everybody's hands, thereby improving the reliability and security of social networks used worldwide. In our study, we came across (1) one known limitation of the current GitLab CE and (2) 6,907 errors, of which 40.16% are beyond our debugging skills.

翻译：在线社交网络已成为我们日常生活中不可或缺的一部分，并在塑造我们与他人关系中发挥着关键作用。然而，即使是微小的程序错误和故障也可能引发从令人沮丧的问题到严重数据泄露等一系列后果，对数百万用户产生深远影响。为降低这些风险，模糊测试——一种使用随机输入进行测试的方法——能够增强对社交网络正常运行的信心。然而，对于社交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其困难或不切实际。为应对这一挑战，我们提出Socialz这一创新的社交模糊测试方法，其具备以下特点：(1) 对社交网络真实用户进行特征刻画，(2) 通过跨多个非平凡特征的进化计算实现用户交互的多样化，(3) 在执行这些交互时收集性能数据。通过Socialz，我们致力于让每个人都能使用社交测试工具，从而提升全球社交网络的可靠性与安全性。在本研究中，我们发现了(1)当前GitLab CE的一个已知限制，以及(2) 6,907个错误，其中40.16%超出了我们的调试能力范围。