Challenges in using Machine Learning to Support Software Engineering

Olimar Borges, Julia Couto, Duncan Ruiz, Rafael Prikladnicki

2021

Abstract

In the past few years, software engineering has increasingly automating several tasks, and machine learning tools and techniques are among the main used strategies to assist in this process. However, there are still challenges to be overcome so that software engineering projects can increasingly benefit from machine learning. In this paper, we seek to understand the main challenges faced by people who use machine learning to assist in their software engineering tasks. To identify these challenges, we conducted a Systematic Review in eight online search engines to identify papers that present the challenges they faced when using machine learning techniques and tools to execute software engineering tasks. Therefore, this research focuses on the classification and discussion of eight groups of challenges: data labeling, data inconsistency, data costs, data complexity, lack of data, non-transferable results, parameterization of the models, and quality of the models. Our results can be used by people who intend to start using machine learning in their software engineering projects to be aware of the main issues they can face.

Paper Citation

in Harvard Style

Borges O., Couto J., Ruiz D. and Prikladnicki R. (2021). Challenges in using Machine Learning to Support Software Engineering. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-509-8, pages 224-231. DOI: 10.5220/0010429402240231

in Bibtex Style

@conference{iceis21,
author={Olimar Borges and Julia Couto and Duncan Ruiz and Rafael Prikladnicki},
title={Challenges in using Machine Learning to Support Software Engineering},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2021},
pages={224-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010429402240231},
isbn={978-989-758-509-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Challenges in using Machine Learning to Support Software Engineering
SN - 978-989-758-509-8
AU - Borges O.
AU - Couto J.
AU - Ruiz D.
AU - Prikladnicki R.
PY - 2021
SP - 224
EP - 231
DO - 10.5220/0010429402240231