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Article

Cognitive Service Virtualisation: A New Machine Learning-Based Virtualisation to Generate Numeric Values

1
School of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia
2
School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Australia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5664; https://doi.org/10.3390/s20195664
Received: 31 August 2020 / Revised: 28 September 2020 / Accepted: 29 September 2020 / Published: 3 October 2020
(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)
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. View Full-Text
Keywords: service virtualisation; machine learning; cognitive system; quality assurance service virtualisation; machine learning; cognitive system; quality assurance
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MDPI and ACS Style

Farahmandpour, Z.; Seyedmahmoudian, M.; Stojcevski, A.; Moser, I.; Schneider, J.-G. Cognitive Service Virtualisation: A New Machine Learning-Based Virtualisation to Generate Numeric Values. Sensors 2020, 20, 5664. https://doi.org/10.3390/s20195664

AMA Style

Farahmandpour Z, Seyedmahmoudian M, Stojcevski A, Moser I, Schneider J-G. Cognitive Service Virtualisation: A New Machine Learning-Based Virtualisation to Generate Numeric Values. Sensors. 2020; 20(19):5664. https://doi.org/10.3390/s20195664

Chicago/Turabian Style

Farahmandpour, Zeinab, Mehdi Seyedmahmoudian, Alex Stojcevski, Irene Moser, and Jean-Guy Schneider. 2020. "Cognitive Service Virtualisation: A New Machine Learning-Based Virtualisation to Generate Numeric Values" Sensors 20, no. 19: 5664. https://doi.org/10.3390/s20195664

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