Legion: Best-first concolic testing (competition contribution)

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    Author
    Liu, D; Ernst, G; Murray, T; Rubinstein, BIP
    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). 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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