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

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Version 2 2024-06-05, 01:58
Version 1 2020-10-10, 16:44
journal contribution
posted on 2024-06-05, 01:58 authored by Z Farahmandpour, M Seyedmahmoudian, A Stojcevski, I Moser, Jean-Guy SchneiderJean-Guy Schneider
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.

History

Journal

Sensors (Switzerland)

Volume

20

Article number

ARTN 5664

Pagination

1-26

Location

Switzerland

ISSN

1424-8220

eISSN

1424-8220

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2020, The Authors

Issue

19

Publisher

MDPI