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QREME – Quality requirements management model for supporting decision-making

Wnuk, Krzysztof LU and Olsson, Thomas (2018) 24th International Working Conference on Requirements Engineering Foundation for Software Quality, REFSQ 2018 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10753 LNCS. p.173-188
Abstract

[Context and motivation] Quality requirements (QRs) are inherently difficult to manage as they are often subjective, context-dependent and hard to fully grasp by various stakeholders. Furthermore, there are many sources that can provide input on important QRs and suitable levels. Responding timely to customer needs and realizing them in product portfolio and product scope decisions remain the main challenge. [Question/problem] Data-driven methodologies based on product usage data analysis gain popularity and enable new (bottom-up, feedback-driven) ways of planning and evaluating QRs in product development. Can these be efficiently combined with established top-down, forward-driven management of QRs? [Principal idea/Results] We propose a... (More)

[Context and motivation] Quality requirements (QRs) are inherently difficult to manage as they are often subjective, context-dependent and hard to fully grasp by various stakeholders. Furthermore, there are many sources that can provide input on important QRs and suitable levels. Responding timely to customer needs and realizing them in product portfolio and product scope decisions remain the main challenge. [Question/problem] Data-driven methodologies based on product usage data analysis gain popularity and enable new (bottom-up, feedback-driven) ways of planning and evaluating QRs in product development. Can these be efficiently combined with established top-down, forward-driven management of QRs? [Principal idea/Results] We propose a model for how to handle decisions about QRs at a strategic and operational level, encompassing product decisions as well as business intelligence and usage data. We inferred the model from an extensive empirical investigation of five years of decision making history at a large B2C company. We illustrate the model by assessing two industrial case studies from different domains. [Contribution] We believe that utilizing the right approach in the right situation will be key for handling QRs, as both different groups of QRs and domains have their special characteristics.

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Please use this url to cite or link to this publication:
author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Non-functional requirements, Quality requirements, Requirements engineering, Requirements scoping
host publication
Requirements Engineering
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume
10753 LNCS
pages
16 pages
publisher
Springer
conference name
24th International Working Conference on Requirements Engineering Foundation for Software Quality, REFSQ 2018
conference location
Utrecht, Netherlands
conference dates
2018-03-19 - 2018-03-22
external identifiers
  • scopus:85043396113
ISSN
0302-9743
1611-3349
ISBN
978-3-319-77243-1
978-3-319-77242-4
DOI
10.1007/978-3-319-77243-1_11
language
English
LU publication?
no
id
27cee734-6ef9-41e5-ac99-536e5a0c11f8
date added to LUP
2018-09-27 14:18:31
date last changed
2018-11-21 21:41:52
@inproceedings{27cee734-6ef9-41e5-ac99-536e5a0c11f8,
  abstract     = {<p>[Context and motivation] Quality requirements (QRs) are inherently difficult to manage as they are often subjective, context-dependent and hard to fully grasp by various stakeholders. Furthermore, there are many sources that can provide input on important QRs and suitable levels. Responding timely to customer needs and realizing them in product portfolio and product scope decisions remain the main challenge. [Question/problem] Data-driven methodologies based on product usage data analysis gain popularity and enable new (bottom-up, feedback-driven) ways of planning and evaluating QRs in product development. Can these be efficiently combined with established top-down, forward-driven management of QRs? [Principal idea/Results] We propose a model for how to handle decisions about QRs at a strategic and operational level, encompassing product decisions as well as business intelligence and usage data. We inferred the model from an extensive empirical investigation of five years of decision making history at a large B2C company. We illustrate the model by assessing two industrial case studies from different domains. [Contribution] We believe that utilizing the right approach in the right situation will be key for handling QRs, as both different groups of QRs and domains have their special characteristics.</p>},
  author       = {Wnuk, Krzysztof and Olsson, Thomas},
  booktitle    = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  isbn         = {978-3-319-77243-1},
  issn         = {0302-9743},
  keyword      = {Non-functional requirements,Quality requirements,Requirements engineering,Requirements scoping},
  language     = {eng},
  location     = {Utrecht, Netherlands},
  month        = {01},
  pages        = {173--188},
  publisher    = {Springer},
  title        = {QREME – Quality requirements management model for supporting decision-making},
  url          = {http://dx.doi.org/10.1007/978-3-319-77243-1_11},
  volume       = {10753 LNCS},
  year         = {2018},
}