Interprocedural Path Complexity Analysis
Mira Bhagirathi Kaniyur, Ana Cavalcante-Studart, Yihan Yang, Sangeon Park, David Chen, Duy Lam, Lucas Bang
Abstract
Software testing techniques like symbolic execution face significant challenges with path explosion. Asymptotic Path Complexity (APC) quantifies this path explosion complexity, but existing APC methods do not work for interprocedural functions in general. Our new algorithm, APC-IP, efficiently computes APC for a wider range of functions, including interprocedural ones, improving over previous methods in both speed and scope. We implement APC-IP atop the existing software Metrinome, and test it against a benchmark of C functions, comparing it to existing and baseline approaches as well as comparing it to the path explosion of the symbolic execution engine Klee. The results show that APC-IP not only aligns with previous APC values but also excels in performance, scalability, and handling complex source code. It also provides a complexity prediction of the number of paths explored by Klee, extending the APC metric's applicability and surpassing previous implementations.
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