A success story in applying data science in practice

A. Bener; B. Turhan; A. Tosun; B. Caglayan; E. Kocaguneli

doi:10.1016/B978-0-12-804206-9.00017-9

A success story in applying data science in practice

A. Bener, B. Turhan, A. Tosun, B. Caglayan, E. Kocaguneli

Department of Computer Engineering

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

Abstract

In software engineering, the primary objective is delivering high-quality systems within budget and time constraints. Managers struggle to make many decisions under a lot of uncertainty. They would like to be confident in the product, team, and the processes. Therefore, the need for evidence-based decision making, a.k.a. data science and analysis, has been growing in the software development industry as data becomes available. Data science involves analytics for using data to understand the past and present, to analyze past performance, and for using optimization and/or prediction techniques.

Original language	English
Title of host publication	Perspectives on Data Science for Software Engineering
Publisher	Elsevier
Pages	85-90
Number of pages	6
ISBN (Electronic)	9780128042069
ISBN (Print)	9780128042618
DOIs	https://doi.org/10.1016/B978-0-12-804206-9.00017-9
Publication status	Published - 1 Jan 2016

Bibliographical note

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

Keywords

Confidence factor modeling
Data collection
Data science
Learning-based predictive models
Measurement and data extraction tool
Model selection
Problem selection
Tool support

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10.1016/B978-0-12-804206-9.00017-9

Cite this

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AU - Turhan, B.

AU - Tosun, A.

AU - Caglayan, B.

AU - Kocaguneli, E.

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AB - In software engineering, the primary objective is delivering high-quality systems within budget and time constraints. Managers struggle to make many decisions under a lot of uncertainty. They would like to be confident in the product, team, and the processes. Therefore, the need for evidence-based decision making, a.k.a. data science and analysis, has been growing in the software development industry as data becomes available. Data science involves analytics for using data to understand the past and present, to analyze past performance, and for using optimization and/or prediction techniques.

KW - Confidence factor modeling

KW - Data collection

KW - Data science

KW - Learning-based predictive models

KW - Measurement and data extraction tool

KW - Model selection

KW - Problem selection

KW - Tool support

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SN - 9780128042618

SP - 85

EP - 90

BT - Perspectives on Data Science for Software Engineering

PB - Elsevier

ER -

A success story in applying data science in practice

Abstract

Bibliographical note

Keywords

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