If it is software engineering, it is (probably) a Bayesian factor

A. Bener; A. Tosun

doi:10.1016/B978-0-12-804206-9.00035-0

If it is software engineering, it is (probably) a Bayesian factor

A. Bener, A. Tosun

Bilgisayar Mühendisliği Bölümü

Toronto Metropolitan University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Bölüm › bilirkişi

1 Atıf (Scopus)

Özet

For more than two decades, we have witnessed how software is developed in the industry and later conducted empirical research in academia to build predictive models, mine software repositories, and perform various software analytics tasks to help software development teams make evidence-based decisions.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Perspectives on Data Science for Software Engineering
Yayınlayan	Elsevier
Sayfalar	187-191
Sayfa sayısı	5
ISBN (Elektronik)	9780128042069
ISBN (Basılı)	9780128042618
DOI'lar	https://doi.org/10.1016/B978-0-12-804206-9.00035-0
Yayın durumu	Yayınlandı - 1 Oca 2016

Bibliyografik not

Publisher Copyright:
© 2016 Elsevier Inc. All rights reserved.

Belgeye Erişim

10.1016/B978-0-12-804206-9.00035-0

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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title = "If it is software engineering, it is (probably) a Bayesian factor",

abstract = "For more than two decades, we have witnessed how software is developed in the industry and later conducted empirical research in academia to build predictive models, mine software repositories, and perform various software analytics tasks to help software development teams make evidence-based decisions.",

keywords = "Artificial intelligence (AI), Bayesian Networks (BN), Causal relationships, Gibbs sampling, Hybrid Bayesian Network",

author = "A. Bener and A. Tosun",

year = "2016",

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doi = "10.1016/B978-0-12-804206-9.00035-0",

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AU - Tosun, A.

PY - 2016/1/1

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N2 - For more than two decades, we have witnessed how software is developed in the industry and later conducted empirical research in academia to build predictive models, mine software repositories, and perform various software analytics tasks to help software development teams make evidence-based decisions.

AB - For more than two decades, we have witnessed how software is developed in the industry and later conducted empirical research in academia to build predictive models, mine software repositories, and perform various software analytics tasks to help software development teams make evidence-based decisions.

KW - Artificial intelligence (AI)

KW - Bayesian Networks (BN)

KW - Causal relationships

KW - Gibbs sampling

KW - Hybrid Bayesian Network

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U2 - 10.1016/B978-0-12-804206-9.00035-0

DO - 10.1016/B978-0-12-804206-9.00035-0

M3 - Chapter

AN - SCOPUS:85115210128

SN - 9780128042618

SP - 187

EP - 191

BT - Perspectives on Data Science for Software Engineering

PB - Elsevier

ER -

If it is software engineering, it is (probably) a Bayesian factor

Özet

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap