Legion: Best-First Concolic Testing (Competition Contribution)
Dongge Liu, Gidon Ernst, Toby Murray, Benjamin I. P. Rubinstein · Lecture notes in computer science · 2020
Legion is a grey-box coverage-based concolic tool that aims to balance the complementary nature of fuzzing and symbolic execution to achieve the best of both worlds. It proposes a variation of Monte Carlo tree search (MCTS) that formulates program exploration as sequential decision-making under uncertainty guided by the best-first search strategy. It relies on approximate path-preserving fuzzing , a novel instance of constrained random testing, which quickly generates many diverse inputs that likely target program parts of interest. In Test-Comp 2020 [ 1 ], the prototype performed within 90% of the best score in 9 of 22 categories.