Now showing items 1-3 of 3

    • BanditFuzz: Fuzzing SMT Solvers with Reinforcement Learning 

      Scott, Joseph; Mora, Federico; Ganesh, Vijay (2020)
      Satisfiability Modulo Theories (SMT) solvers are fundamental tools in the broad context of software engineering and security research. If SMT solvers are to continue to have an impact, it is imperative we develop efficient ...
    • MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers 

      Scott, Joseph; Niemetz, Aina; Preiner, Mathias; Ganesh, Vijay (2020)
      In this paper, we present MachSMT, an algorithm selection tool for state-of-the-art Satisfiability Modulo Theories (SMT) solvers. MachSMT supports the entirety of the logics within the SMT-LIB initiative. MachSMT uses ...
    • Verifying Mutable Systems 

      Scott, Joseph (University of Waterloo, 2017-10-23)
      Model checking has had much success in the verification of single-process and multi-process programs. However, model checkers assume an immutable topology which limits the verification in several areas. Consider the security ...

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