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We describe a new blackbox complexity testing technique for determining the worst-case asymptotic complexity of a given application. The key idea is to look for an input pattern —rather than a concrete input— that maximizes the asymptotic resource usage of the target program. Because input patterns can be described concisely as programs in a restricted language, our method transforms the complexity testing problem to optimal program synthesis. In particular, we express these input patterns using a new model of computation called Recurrent Computation Graph (RCG) and solve the optimal synthesis problem by developing a genetic programming algorithm that operates on RCGs. We have implemented the proposed ideas in a tool called Singularityand evaluate it on a diverse set of benchmarks. Our evaluation shows that Singularitycan effectively discover the worst-case complexity of various algorithms and that it is more scalable compared to existing state-of-the-art techniques. Furthermore, our experiments also corroborate that Singularitycan discover previously unknown performance bugs and availability vulnerabilities in real-world applications such as Google Guava and JGraphT.

Singularity: pattern fuzzing for worst case complexity