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Classifying collaborative approaches for cloud based collaborative software development

Ewenike, Stanley, BENKHELIFA, Elhadj and CHIBELUSHI, Claude (2018) Classifying collaborative approaches for cloud based collaborative software development. In: Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS). IEEE, pp. 47-52. ISBN 978-1-5386-3148-5

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Official URL: http://dx.doi.org/10.1109/FADS.2017.8253192

Abstract or description

Software development is an activity involving a remix set of different people, tools, practice culture, etcetera, and working towards an end goal. Achieving the goal necessitates that all these aspects work together towards the end goal. Furthermore, the size, complexity, longevity and tight delivery timelines of software projects, form part of the rationale for collaboration in software development processes. With the advent of Cloud computing, these factors have become more pronounced. Other factors such as increased distribution, have also become part of the rationale increasing the need for better collaborative approaches. Collaboration can take numerous forms and dimensions, but that does not necessarily mean that any form of collaborative approach is good for every scenario or context. There is no “one size fits all” approach. Different development contexts may require different collaborative approaches for greater effectiveness. So, which collaborative approach is right, and which is wrong, for Cloud-based software development lifecycle? This paper reviews literature with an aim of presenting a classification for collaborative approaches towards context-aware Cloud-based software development.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: “© © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.”
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Stanley EWENIKE
Date Deposited: 15 Feb 2019 09:43
Last Modified: 24 Feb 2023 13:53
Related URLs:
URI: https://eprints.staffs.ac.uk/id/eprint/5175

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