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Characteristics that affect Preference of Decision Models for Asset Selection: An Industrial Questionnaire Survey
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. (SERL)
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. (SERL)
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0002-1532-8223
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. (SERL)
2020 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 28, no 4, p. 1675-1707Article in journal (Refereed) Published
Abstract [en]

Modern software development relies on a combination of development and re-use of technical asset, e.g. software components, libraries and APIs.In the past, re-use was mostly conducted with internal assets but today external; open source, customer off-the-shelf (COTS) and assets developed through outsourcing are also common.This access to more asset alternatives presents new challenges regarding what assets to optimally chose and how to make this decision.To support decision-makers, decision-theory has been used to develop decision models for asset selection.However, very little industrial data has been presented in literature about the usefulness, or even perceived usefulness, of these models.Additionally, only limited information has been presented about what model characteristics that determine practitioner preference towards one model over another.

Objective: The objective of this work is to evaluate what characteristics of decision models for asset selection that determine industrial practitioner preference of a model when given the choice of a decision-model of high precision or a model with high speed.

Method: An industrial questionnaire survey is performed where a total of 33 practitioners, of varying roles, from 18 companies are tasked to compare two decision models for asset selection.Textual analysis and formal and descriptive statistics are then applied on the survey responses to answer the study's research questions.

Results: The study shows that the practitioners had clear preference towards the decision model that emphasised speed over the one that emphasised decision precision.This conclusion was determined to be because one of the models was perceived faster, had lower complexity, had, was more flexible in use for different decisions, was more agile how it could be used in operation, its emphasis on people, its emphasis on ``good enough'' precision and ability to fail fast if a decision was a failure.Hence, seven characteristics that the practitioners considered important for their acceptance of the model.

Conclusion: Industrial practitioner preference, which relates to acceptance, of decision models for asset selection is dependent on multiple characteristics that must be considered when developing a model for different types of decisions such as operational day-to-day decisions as well as more critical tactical or strategic decisions.The main contribution of this work are seven identified characteristics that can serve as industrial requirements for future research on decision models for asset selection.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 28, no 4, p. 1675-1707
Keywords [en]
Decision-models, Characteristics, Industrial study, Survey, Model comparison
National Category
Computer Systems Software Engineering
Identifiers
URN: urn:nbn:se:bth-18687DOI: 10.1007/s11219-019-09489-8ISI: 000504580300001OAI: oai:DiVA.org:bth-18687DiVA, id: diva2:1354445
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ORION
Funder
Knowledge Foundation, 20140218
Note

open access

Available from: 2019-09-25 Created: 2019-09-25 Last updated: 2022-05-25Bibliographically approved

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Alégroth, EmilGorschek, TonyPetersen, KaiMattsson, Michael

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Alégroth, E., Gorschek, T., Petersen, K. & Mattsson, M. (2019). Characteristics that affect Preference of Decision Models for Asset Selection: An Industrial Questionnaire Survey - Appendix A: Questionnaire Introduction. Decision-making in Practice / Appendix B: Survey results.

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