On the use of machine learning for predicting defect fix time violations

Name: On the use of machine learning for predicting defect fix time violations
Author: Kanoğlu, Ümit, Dolaş, Can, Sözer, Hasan

İsim	On the use of machine learning for predicting defect fix time violations
Yazar	Kanoğlu, Ümit, Dolaş, Can, Sözer, Hasan
Basım Tarihi:	2022
Basım Yeri	- Science and Technology Publications
Konu	Bug fix time prediction, Classification, Fix time violation, Industrial case study, Machine learning
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-989758568-5
Kayıt Numarası	2cc1e925-937f-45bf-9fce-70d054ed0eb8
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	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.
DOI	10.5220/0011059900003176

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On the use of machine learning for predicting defect fix time violations

Yazar Kanoğlu, Ümit, Dolaş, Can, Sözer, Hasan

Basım Tarihi 2022

Basım Yeri - Science and Technology Publications

Konu Bug fix time prediction, Classification, Fix time violation, Industrial case study, Machine learning

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-989758568-5

Kayıt Numarası 2cc1e925-937f-45bf-9fce-70d054ed0eb8

Lokasyon Computer Science

Tarih 2022

Örnek Metin 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.

DOI 10.5220/0011059900003176