QREME – Quality requirements management model for supporting decision-making
(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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- author
- Wnuk, Krzysztof LU and Olsson, Thomas
- publishing date
- 2018-01-01
- 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 : Foundation for Software Quality - 24th International Working Conference, REFSQ 2018, Proceedings - Foundation for Software Quality - 24th International Working Conference, REFSQ 2018, Proceedings
- 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
- 978-3-319-77242-4
- 978-3-319-77243-1
- DOI
- 10.1007/978-3-319-77243-1_11
- project
- Embedded Applications Software Engineering
- language
- English
- LU publication?
- no
- id
- 27cee734-6ef9-41e5-ac99-536e5a0c11f8
- date added to LUP
- 2018-09-27 14:18:31
- date last changed
- 2024-03-18 14:45:43
@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 = {{Requirements Engineering : Foundation for Software Quality - 24th International Working Conference, REFSQ 2018, Proceedings}},
isbn = {{978-3-319-77242-4}},
issn = {{0302-9743}},
keywords = {{Non-functional requirements; Quality requirements; Requirements engineering; Requirements scoping}},
language = {{eng}},
month = {{01}},
pages = {{173--188}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{QREME – Quality requirements management model for supporting decision-making}},
url = {{http://dx.doi.org/10.1007/978-3-319-77243-1_11}},
doi = {{10.1007/978-3-319-77243-1_11}},
volume = {{10753 LNCS}},
year = {{2018}},
}