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Authors: Olimar Teixeira Borges ; Julia Colleoni Couto ; Duncan Dubugras A. Ruiz and Rafael Prikladnicki

Affiliation: School of Technology, PUCRS, Porto Alegre, Brazil

Keyword(s): Software Engineering, Machine Learning, Mapping Study.

Abstract: Machine Learning (ML) environments are composed of a set of techniques and tools, which can help in solving problems in a diversity of areas, including Software Engineering (SE). However, due to a large number of possible configurations, it is a challenge to select the ML environment to be used for a specific SE domain issue. Helping software engineers choose the most suitable ML environment according to their needs would be very helpful. For instance, it is possible to automate software tests using ML models, where the model learns software behavior and predicts possible problems in the code. In this paper, we present a mapping study that categorizes the ML techniques and tools reported as useful to solve SE domain issues. We found that the most used algorithm is Naíve Bayes and that WEKA is the tool most SE researchers use to perform ML experiments related to SE. We also identified that most papers use ML to solve problems related to SE quality. We propose a categorization of the M L techniques and tools that are applied in SE problem solving, linking with the Software Engineering Body of Knowledge (SWEBOK) knowledge areas. (More)

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Paper citation in several formats:
Borges, O.; Couto, J.; Ruiz, D. and Prikladnicki, R. (2020). How Machine Learning Has Been Applied in Software Engineering?. In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-423-7; ISSN 2184-4992, SciTePress, pages 306-313. DOI: 10.5220/0009417703060313

@conference{iceis20,
author={Olimar Teixeira Borges. and Julia Colleoni Couto. and Duncan Dubugras A. Ruiz. and Rafael Prikladnicki.},
title={How Machine Learning Has Been Applied in Software Engineering?},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2020},
pages={306-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009417703060313},
isbn={978-989-758-423-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - How Machine Learning Has Been Applied in Software Engineering?
SN - 978-989-758-423-7
IS - 2184-4992
AU - Borges, O.
AU - Couto, J.
AU - Ruiz, D.
AU - Prikladnicki, R.
PY - 2020
SP - 306
EP - 313
DO - 10.5220/0009417703060313
PB - SciTePress

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