  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.
