Inferring Computational State Machine Models from Program Executions

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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",
• month = oct,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://bibtex.github.io/ICSME-2016-WalkinshawH.html",
• URL = "https://eprints.whiterose.ac.uk/127869/1/ICSME2016FinalSubmission.pdf",
• DOI = "doi:10.1109/ICSME.2016.74",
• size = "11 pages",
• 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.",
• notes = "Also known as \cite{7816460}",

}

Genetic Programming entries for Neil Walkinshaw Mathew Hall

Citations