Inferring Computational State Machine Models from Program Executions

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@InProceedings{Walkinshaw:2016:ICSME,
  author =       "Neil Walkinshaw and Mathew Hall",
  booktitle =    "2016 IEEE International Conference on Software
                 Maintenance and Evolution (ICSME)",
  title =        "Inferring Computational State Machine Models from
                 Program Executions",
  year =         "2016",
  pages =        "122--132",
  abstract =     "The challenge of inferring state machines from log
                 data or execution traces is well-established, and has
                 led to the development of several powerful techniques.
                 Current approaches tend to focus on the inference of
                 conventional finite state machines or, in few cases,
                 state machines with guards. However, these machines are
                 ultimately only partial, because they fail to model how
                 any underlying variables are computed during the course
                 of an execution, they are not computational. In this
                 paper we introduce a technique based upon Genetic
                 Programming to infer these data transformation
                 functions, which in turn render inferred automata fully
                 computational. Instead of merely determining whether or
                 not a sequence is possible, they can be simulated, and
                 be used to compute the variable values throughout the
                 course of an execution. We demonstrate the approach by
                 using a Cross-Validation study to reverse-engineer
                 complete (computational) EFSMs from traces of
                 established implementations.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICSME.2016.74",
  month =        oct,
  notes =        "Also known as \cite{7816460}",
}

Genetic Programming entries for Neil Walkinshaw Mathew Hall

Citations