Hall, M. and Walkinshaw, N. (2017) Data and analysis code for GP EFSM inference. In: 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME). 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME) , 02-07 Oct 2016, Raleigh, NC, USA. IEEE , p. 611. ISBN 978-1-5090-3806-0
Abstract
This artifact 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).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Mar 2018 10:19 |
Last Modified: | 09 Mar 2018 10:23 |
Published Version: | https://doi.org/10.1109/ICSME.2016.22 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICSME.2016.22 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127867 |