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

Department of Computer Engineering

Toronto Metropolitan University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Perspectives on Data Science for Software Engineering
Publisher	Elsevier
Pages	187-191
Number of pages	5
ISBN (Electronic)	9780128042069
ISBN (Print)	9780128042618
DOIs	https://doi.org/10.1016/B978-0-12-804206-9.00035-0
Publication status	Published - 1 Jan 2016

Bibliographical note

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

Keywords

Artificial intelligence (AI)
Bayesian Networks (BN)
Causal relationships
Gibbs sampling
Hybrid Bayesian Network

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

Cite this

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KW - Causal relationships

KW - Gibbs sampling

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

Abstract

Bibliographical note

Keywords

Access to Document

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