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Exploring factors affecting decision outcome and lead time in large-scale requirements engineering

Wnuk, Krzysztof LU ; Kabbedijk, Jaap; Brinkkemper, Sjaak; Regnell, Björn LU and Callele, David (2015) In Journal of software: Evolution and Process 27(9). p.647-673
Abstract
Lead time, defined as the duration between the moment a request was filed and the moment the decision was made, is an important aspect of decision making in market-driven requirements engineering. Minimizing lead time allows software companies to focus their resources on the most profitable functionality and enables them to remain competitive within the quickly changing software market. Achieving and sustaining low decision lead time and the resulting high decision efficiency require a better understanding of factors that may affect both decision lead time and outcome. In order to identify possible factors, we conducted an exploratory two-stage case study that combines the statistical analysis of seven possible relationships among decision... (More)
Lead time, defined as the duration between the moment a request was filed and the moment the decision was made, is an important aspect of decision making in market-driven requirements engineering. Minimizing lead time allows software companies to focus their resources on the most profitable functionality and enables them to remain competitive within the quickly changing software market. Achieving and sustaining low decision lead time and the resulting high decision efficiency require a better understanding of factors that may affect both decision lead time and outcome. In order to identify possible factors, we conducted an exploratory two-stage case study that combines the statistical analysis of seven possible relationships among decision characteristics at a large company with a survey of industry participants. Our results show that the number of products affected by a decision increases the time needed to make a decision. Practitioners should take this aspect into consideration when planning for efficient decision making and possibly reducing the complexity of decisions. Our results also show that when a change request originates from an important customer, the request is more often accepted. The results provide input into the discussion of whether a large company should focus on only a few of its large customers and disregard its significantly larger group of small customers. The results provide valuable insights for researchers, who can use them to plan research of decision-making processes and methods, and for practitioners, who can use them to optimize their decision-making processes. In future work, we plan to investigate other decision characteristics, such as the number of stakeholders involved in the discussion about the potential change or the number of dependencies between software components. Copyright (C) 2015 John Wiley & Sons, Ltd. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
requirements engineering, decision making, market-driven requirements, engineering, software product lines
in
Journal of software: Evolution and Process
volume
27
issue
9
pages
647 - 673
publisher
John Wiley & Sons
external identifiers
  • wos:000362500800003
  • scopus:84941654310
ISSN
2047-7481
DOI
10.1002/smr.1721
language
English
LU publication?
yes
id
26255695-2673-43df-97d7-43264b45e4cd (old id 8220427)
date added to LUP
2015-11-27 15:04:16
date last changed
2017-01-01 03:07:26
@article{26255695-2673-43df-97d7-43264b45e4cd,
  abstract     = {Lead time, defined as the duration between the moment a request was filed and the moment the decision was made, is an important aspect of decision making in market-driven requirements engineering. Minimizing lead time allows software companies to focus their resources on the most profitable functionality and enables them to remain competitive within the quickly changing software market. Achieving and sustaining low decision lead time and the resulting high decision efficiency require a better understanding of factors that may affect both decision lead time and outcome. In order to identify possible factors, we conducted an exploratory two-stage case study that combines the statistical analysis of seven possible relationships among decision characteristics at a large company with a survey of industry participants. Our results show that the number of products affected by a decision increases the time needed to make a decision. Practitioners should take this aspect into consideration when planning for efficient decision making and possibly reducing the complexity of decisions. Our results also show that when a change request originates from an important customer, the request is more often accepted. The results provide input into the discussion of whether a large company should focus on only a few of its large customers and disregard its significantly larger group of small customers. The results provide valuable insights for researchers, who can use them to plan research of decision-making processes and methods, and for practitioners, who can use them to optimize their decision-making processes. In future work, we plan to investigate other decision characteristics, such as the number of stakeholders involved in the discussion about the potential change or the number of dependencies between software components. Copyright (C) 2015 John Wiley & Sons, Ltd.},
  author       = {Wnuk, Krzysztof and Kabbedijk, Jaap and Brinkkemper, Sjaak and Regnell, Björn and Callele, David},
  issn         = {2047-7481},
  keyword      = {requirements engineering,decision making,market-driven requirements,engineering,software product lines},
  language     = {eng},
  number       = {9},
  pages        = {647--673},
  publisher    = {John Wiley & Sons},
  series       = {Journal of software: Evolution and Process},
  title        = {Exploring factors affecting decision outcome and lead time in large-scale requirements engineering},
  url          = {http://dx.doi.org/10.1002/smr.1721},
  volume       = {27},
  year         = {2015},
}