Legion: Best-first concolic testing (competition contribution)
AuthorLiu, D; Ernst, G; Murray, T; Rubinstein, BIP
Source TitleLecture Notes in Artificial Intelligence
AffiliationComputing and Information Systems
Document TypeConference Paper
CitationsLiu, D., Ernst, G., Murray, T. & Rubinstein, B. I. P. (2020). Legion: Best-first concolic testing (competition contribution). 23rd International Conference, FASE 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25–30, 2020, Proceedings, 12076 LNCS, pp.545-549. Springer. https://doi.org/10.1007/978-3-030-45234-6_31.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418122
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 , the prototype performed within 90% of the best score in 9 of 22 categories.
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