Developer Modelling using Software Quality Metrics and Machine Learning

Franciele Beal, Patricia Rucker de Bassi, Emerson Cabrera Paraiso

2017

Abstract

Software development has become an essential activity for organizations that increasingly rely on these to manage their business. However, poor software quality reduces customer satisfaction, while high-quality software can reduce repairs and rework by more than 50 percent. Software development is now seen as a collaborative and technology-dependent activity performed by a group of people. For all these reasons, choosing correctly software development members teams can be decisive. Considering this motivation, classifying participants in different profiles can be useful during project management team’s formation and tasks distribution. This paper presents a developer modeling approach based on software quality metrics. Quality metrics are dynamically collected. Those metrics compose the developer model. A machine learning-based method is presented. Results show that it is possible to use quality metrics to model developers.

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Paper Citation


in Harvard Style

Beal F., Rucker de Bassi P. and Cabrera Paraiso E. (2017). Developer Modelling using Software Quality Metrics and Machine Learning . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 424-432. DOI: 10.5220/0006327104240432


in Bibtex Style

@conference{iceis17,
author={Franciele Beal and Patricia Rucker de Bassi and Emerson Cabrera Paraiso},
title={Developer Modelling using Software Quality Metrics and Machine Learning},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={424-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006327104240432},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Developer Modelling using Software Quality Metrics and Machine Learning
SN - 978-989-758-247-9
AU - Beal F.
AU - Rucker de Bassi P.
AU - Cabrera Paraiso E.
PY - 2017
SP - 424
EP - 432
DO - 10.5220/0006327104240432