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Research paper | Open Access

Definitive guidelines toward effective mobile devices crowdtesting methodology

Qamar Naith( )Fabio Ciravegna
Department of Computer Science, University of Sheffield, Sheffield, UK
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Abstract

Purpose

This paper aims to gauge developers’ perspectives regarding the participation of the public and anonymous crowd testers worldwide, with a range of varied experiences. It also aims to gather their needs that could reduce their concerns of dealing with the public crowd testers and increase the opportunity of using the crowdtesting platforms.

Design/methodology/approach

An online exploratory survey was conducted to gather information from the participants, which included 50 mobile application developers from various countries with diverse experiences across Android and iOS mobile platforms.

Findings

The findings revealed that a significant proportion (90%) of developers is potentially willing to perform testing via the public crowd testers worldwide. This on condition that several fundamental features were available, which enable them to achieve more realistic tests without artificial environments on large numbers of devices. The results also demonstrated that a group of developers does not consider testing as a serious job that they have to pay for, which can affect the gig-economy and global market.

Originality/value

This paper provides new insights for future research in the study of how acceptable it is to work with public and anonymous crowd workers, with varying levels of experience, to perform tasks in different domains and not only in software testing. In addition, it will assist individual or small development teams who have limited resources or who do not have thousands of testers in their private testing community, to perform large-scale testing of their products.

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International Journal of Crowd Science
Pages 209-228
Cite this article:
Naith Q, Ciravegna F. Definitive guidelines toward effective mobile devices crowdtesting methodology. International Journal of Crowd Science, 2020, 4(2): 209-228. https://doi.org/10.1108/IJCS-01-2020-0002

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Received: 21 January 2020
Revised: 25 February 2020
Accepted: 25 February 2020
Published: 28 April 2020
© The author(s)

Qamar Naith and Fabio Ciravegna. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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