Socialz: Multi-Feature Social Fuzz Testing

TL;DR

Socialz introduces a novel social fuzz testing approach that characterizes real users, diversifies interactions through evolutionary computation, and collects performance data to improve social network reliability and security.

Contribution

It presents a new multi-feature social fuzz testing method that is accessible to non-experts and enhances social network testing capabilities.

Findings

Identified 6,907 errors in a social network platform.

Discovered that 40.16% of errors are beyond current debugging skills.

Highlighted a known limitation of GitLab CE.

Abstract

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.