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一个不可分割的方面,在塑造我们与他人的关系方面发挥着关键作用。然而,错误和漏洞,即使是轻微的错误和漏洞,都可能导致从令人沮丧的问题到可能对数百万用户产生深远影响的严重数据泄漏等任何问题。为减轻这些风险,通过随机化投入进行模糊测试这一测试方法可以使人们更加相信社会网络的正确运作。然而,对网络发展团队以外的程序员来说,采用传统的模糊测试方法可能极其困难或不切实际。为了应对这一挑战,我们介绍社会奇特的社交测试方法,即:(1) 描述社会网络的真正用户,(2) 利用跨多个非三角特征的进化计算,以及(3) 在进行这些互动时收集性能数据,以分散他们的互动。我们与社会兹合作,旨在向任何人提供进行全面社会测试的能力,从而提高世界各地使用的在线社交网络的可靠性和安全性。</s>