How Machine Learning Has Been Applied in Software Engineering?

Olimar Borges, Julia Couto, Duncan Ruiz, Rafael Prikladnicki

2020

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 ML techniques and tools that are applied in SE problem solving, linking with the Software Engineering Body of Knowledge (SWEBOK) knowledge areas.

Paper Citation

in Harvard Style

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, pages 306-313. DOI: 10.5220/0009417703060313

in Bibtex Style

@conference{iceis20,
author={Olimar Borges and Julia Couto and Duncan 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},
}

in EndNote Style

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
AU - Borges O.
AU - Couto J.
AU - Ruiz D.
AU - Prikladnicki R.
PY - 2020
SP - 306
EP - 313
DO - 10.5220/0009417703060313