Inferring Computational State Machine Models from Program Executions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Walkinshaw:2016:ICSME,
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author = "Neil Walkinshaw and Mathew Hall",
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booktitle = "2016 IEEE International Conference on Software
Maintenance and Evolution (ICSME)",
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title = "Inferring Computational State Machine Models from
Program Executions",
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year = "2016",
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pages = "122--132",
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month = oct,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://bibtex.github.io/ICSME-2016-WalkinshawH.html",
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URL = "https://eprints.whiterose.ac.uk/127869/1/ICSME2016FinalSubmission.pdf",
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DOI = "doi:10.1109/ICSME.2016.74",
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size = "11 pages",
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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.",
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notes = "Also known as \cite{7816460}",
- }
Genetic Programming entries for
Neil Walkinshaw
Mathew Hall
Citations