Data and Analysis Code for GP EFSM Inference

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{Hall:2016:ICSME,

• author = "Mathew Hall and Neil Walkinshaw",
• booktitle = "2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)",
• title = "Data and Analysis Code for GP EFSM Inference",
• year = "2016",
• pages = "611--611",
• abstract = "This artefact captures the workflow that we adopted for our experimental evaluation in our ICSME paper on inferring state transition functions during EFSM inference. To summarise, the paper uses Genetic Programming to infer data transformations, to enable the inference of fully 'computational' extended finite state machine models. This submission shows how we generated, transformed, analysed, and visualised our raw data. It includes everything needed to generate raw results and provides the relevant R code in the form of a re-usable Jupyter Notebook (accompanied by a descriptive narrative).",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICSME.2016.22",
• month = oct,
• notes = "Also known as \cite{7816520}",

}

Genetic Programming entries for Mathew Hall Neil Walkinshaw

Citations