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Cognitive service virtualisation: A new machine learning-based virtualisation to generate numeric values

Farahmandpour, Zeinab, Seyedmahmoudian, Mehdi, Stojcevski, Alex, Moser, Irene and Schneider, Jean-Guy 2020, Cognitive service virtualisation: A new machine learning-based virtualisation to generate numeric values, Sensors, vol. 20, no. 19, doi: 10.3390/s20195664.

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Title Cognitive service virtualisation: A new machine learning-based virtualisation to generate numeric values
Author(s) Farahmandpour, Zeinab
Seyedmahmoudian, Mehdi
Stojcevski, Alex
Moser, Irene
Schneider, Jean-GuyORCID iD for Schneider, Jean-Guy orcid.org/0000-0002-9827-5496
Journal name Sensors
Volume number 20
Issue number 19
Article ID 5664
Total pages 26
Publisher M D P I AG
Place of publication Basel, Switzerland
Publication date 2020-10
ISSN 1424-8220
Keyword(s) service virtualisation
machine learning
cognitive system
quality assurance
Summary Continuous delivery has gained increased popularity in industry as a development approach to develop, test, and deploy enhancements to software components in short development cycles. In order for continuous delivery to be effectively adopted, the services that a component depends upon must be readily available to software engineers in order to systematically apply quality assurance techniques. However, this may not always be possible as (i) these requisite services may have limited access and (ii) defects that are introduced in a component under development may cause ripple effects in real deployment environments. Service virtualisation (SV) has been introduced as an approach to address these challenges, but existing approaches to SV still fall short of delivering the required accuracy and/or ease-of-use to virtualise services for adoption in continuous delivery. In this work, we propose a novel machine learning based approach to predict numeric fields in virtualised responses, extending existing research that has provided a way to produce values for categorical fields. The SV approach introduced here uses machine learning techniques to derive values of numeric fields that are based on a variable number of pertinent historic messages. Our empirical evaluation demonstrates that the Cognitive SV approach can produce responses with the appropriate fields and accurately predict values of numeric fields across three data sets, some of them based on stateful protocols.
Language eng
DOI 10.3390/s20195664
Indigenous content off
Field of Research 0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
0502 Environmental Science and Management
0602 Ecology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30143885

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.