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Authors: Rakesh Rana 1 ; Miroslaw Staron 1 ; Jörgen Hansson 2 ; Martin Nilsson 3 and Wilhelm Meding 4

Affiliations: 1 University of Gothenburg, Sweden ; 2 University of Gothenburg and University of Skövde, Sweden ; 3 Volvo Car Group, Sweden ; 4 Ericsson, Sweden

ISBN: 978-989-758-036-9

Keyword(s): Machine Learning, Software Defect Prediction, Technology Acceptance, Adoption, Software Quality.

Related Ontology Subjects/Areas/Topics: Decision Support Systems ; Enterprise Software Technologies ; Quality Assurance ; Service-Oriented Software Engineering and Management ; Software Engineering ; Software Engineering Methods and Techniques ; Software Metrics ; Software Project Management ; Software Quality Management ; Software Testing and Maintenance

Abstract: Machine learning algorithms are increasingly being used in a variety of application domains including software engineering. While their practical value have been outlined, demonstrated and highlighted in number of existing studies, their adoption in industry is still not widespread. The evaluations of machine learning algorithms in literature seem to focus on few attributes and mainly on predictive accuracy. On the other hand the decision space for adoption or acceptance of machine learning algorithms in industry encompasses much more factors. Companies looking to adopt such techniques want to know where such algorithms are most useful, if the new methods are reliable and cost effective. Further questions such as how much would it cost to setup, run and maintain systems based on such techniques are currently not fully investigated in the industry or in academia leading to difficulties in assessing the business case for adoption of these techniques in industry. In this paper we argue f or the need of framework for adoption of machine learning in industry. We develop a framework for factors and attributes that contribute towards the decision of adoption of machine learning techniques in industry for the purpose of software defect predictions. The framework is developed in close collaboration within industry and thus provides useful insight for industry itself, academia and suppliers of tools and services. (More)

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Paper citation in several formats:
Rana, R.; Staron, M.; Hansson, J.; Nilsson, M. and Meding, W. (2014). A Framework for Adoption of Machine Learning in Industry for Software Defect Prediction.In Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014) ISBN 978-989-758-036-9, pages 383-392. DOI: 10.5220/0005099303830392

@conference{icsoft-ea14,
author={Rakesh Rana. and Miroslaw Staron. and Jörgen Hansson. and Martin Nilsson. and Wilhelm Meding.},
title={A Framework for Adoption of Machine Learning in Industry for Software Defect Prediction},
booktitle={Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014)},
year={2014},
pages={383-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005099303830392},
isbn={978-989-758-036-9},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2014)
TI - A Framework for Adoption of Machine Learning in Industry for Software Defect Prediction
SN - 978-989-758-036-9
AU - Rana, R.
AU - Staron, M.
AU - Hansson, J.
AU - Nilsson, M.
AU - Meding, W.
PY - 2014
SP - 383
EP - 392
DO - 10.5220/0005099303830392

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