TY - CHAP
T1 - A success story in applying data science in practice
AU - Bener, A.
AU - Turhan, B.
AU - Tosun, A.
AU - Caglayan, B.
AU - Kocaguneli, E.
N1 - Publisher Copyright:
© 2016 Elsevier Inc. All rights reserved.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
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
UR - http://www.scopus.com/inward/record.url?scp=85133926404&partnerID=8YFLogxK
U2 - 10.1016/B978-0-12-804206-9.00017-9
DO - 10.1016/B978-0-12-804206-9.00017-9
M3 - Chapter
AN - SCOPUS:85133926404
SN - 9780128042618
SP - 85
EP - 90
BT - Perspectives on Data Science for Software Engineering
PB - Elsevier
ER -