On the Use of Machine Learning for Predicting Defect Fix Time Violations

Ümit Kanoğlu; Ümit Kanoğlu; Can Dolaş; Can Dolaş; Hasan Sözer

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Paper

On the Use of Machine Learning for Predicting Defect Fix Time Violations

Topics: Service Level Agreements (Slas) and Quality of Service (Qos); Software and Service Maintenance and Evolution

In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering ENASE - Volume 1, 119-127, 2022

Authors: Ümit Kanoğlu ^{1

;

2} ; Can Dolaş ^{1

;

2} and Hasan Sözer ¹

Affiliations: ¹ Ozyegin University, Istanbul, Turkey ; ² Turk Telekom, Ankara, Turkey

Keyword(s): Defect Fix Time Prediction, Bug Fix Time Prediction, Fix Time Violation, Classification, Machine Learning, Industrial Case Study.

Abstract: Accurate prediction of defect fix time is important for estimating and coordinating software maintenance efforts. Likewise, it is useful to predict whether or not the initially estimated defect fix time will be exceeded during the maintenance process. We present an empirical evaluation on the use of machine learning for predicting defect fix time violations. We conduct an industrial case study based on real projects from the telecommunications domain. We prepare a dataset with 69,000 defect reports regarding 293 projects being maintained between 2015 and 2021. We employ 7 machine learning algorithms. We experiment with 3 subsets of 25 features derived from defects as well as the corresponding projects. Gradient boosted classifiers perform the best by reaching up to 72% accuracy.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Kanoğlu, Ü.; Dolaş, C. and Sözer, H. (2022). On the Use of Machine Learning for Predicting Defect Fix Time Violations. In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-568-5; ISSN 2184-4895, SciTePress, pages 119-127. DOI: 10.5220/0011059900003176

@conference{enase22,
author={Ümit Kanoğlu. and Can Dolaş. and Hasan Sözer.},
title={On the Use of Machine Learning for Predicting Defect Fix Time Violations},
booktitle={Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2022},
pages={119-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011059900003176},
isbn={978-989-758-568-5},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - On the Use of Machine Learning for Predicting Defect Fix Time Violations
SN - 978-989-758-568-5
IS - 2184-4895
AU - Kanoğlu, Ü.
AU - Dolaş, C.
AU - Sözer, H.
PY - 2022
SP - 119
EP - 127
DO - 10.5220/0011059900003176
PB - SciTePress