Predicting defects with latent and semantic features from commit logs in an industrial setting

Beyza Eken, Rifat Atar, Sahra Sertalp, Ayse Tosun

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

4 Atıf (Scopus)

Özet

Software defect prediction is still a challenging task in industrial settings. Noisy data and/or lack of data make it hard to build successful prediction models. In this study, we aim to build a change-level defect prediction model for a software project in an industrial setting. We combine various probabilistic models, namely matrix factorization and topic modeling, with the expectation of overcoming the noisy and limited nature of industrial settings by extracting hidden features from multiple resources. Commit level process metrics, latent features from commits, and semantic features from commit messages are combined to build the defect predictors with the use of Log Filtering and feature selection techniques, and two machine learning algorithms Naive Bayes and Extreme Gradient Boosting (XGBoost). Collecting data from various sources and applying data pre-processing techniques show a statistically significant improvement in terms of probability of detection by up to 24% when compared to a base model with process metrics only.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2019
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar98-105
Sayfa sayısı8
ISBN (Elektronik)9781728141367
DOI'lar
Yayın durumuYayınlandı - Kas 2019
Etkinlik34th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2019 - San Diego, United States
Süre: 10 Kas 201915 Kas 2019

Yayın serisi

AdıProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2019

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???event.eventtypes.event.conference???34th IEEE/ACM International Conference on Automated Software Engineering Workshops, ASEW 2019
Ülke/BölgeUnited States
ŞehirSan Diego
Periyot10/11/1915/11/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Finansman

This study is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under the project 5170048.

FinansörlerFinansör numarası
TUBITAK5170048
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

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