Legion: Best-first concolic testing (competition contribution)

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    Author
    Liu, D; Ernst, G; Murray, T; Rubinstein, BIP
    Editor
    Wehrheim, H; Cabot, J
    Date
    2020-01-01
    Source Title
    Lecture Notes in Artificial Intelligence
    Publisher
    Springer
    University of Melbourne Author/s
    Rubinstein, Benjamin; Murray, Tobias; Liu, Dongge
    Affiliation
    Computing and Information Systems
    Metadata
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    Document Type
    Conference Paper
    Citations
    Liu, D., Ernst, G., Murray, T. & Rubinstein, B. I. P. (2020). Legion: Best-first concolic testing (competition contribution). Wehrheim, H (Ed.) Cabot, J (Ed.) 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 Status
    Open Access
    URI
    http://hdl.handle.net/11343/252064
    DOI
    10.1007/978-3-030-45234-6_31
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418122
    Abstract
    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.

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