research-article

Resistance to Change in Software Process Improvement - An Investigation of Causes, Effects and Conducts

Authors:

Monica Anastassiu,

Gleison SantosAuthors Info & Claims

SBQS '20: Proceedings of the XIX Brazilian Symposium on Software Quality

Article No.: 21, Pages 1 - 11

https://doi.org/10.1145/3439961.3439982

Published: 06 March 2021 Publication History

Abstract

Context: Resistance to change can affect the effectiveness of software process improvement initiatives as well as the maintenance of improvements after an official assessment. However, the inherent attributes of resistance to change are not entirely clear, for instance: its effects as well as actions to mitigate its occurrence. Objective: We aim to investigate potential causes and effects of resistance to change as well as conducts that can mitigate its occurrence on software processes improvement initiatives. Method: We performed a qualitative study in which we interviewed 21 practitioners and specialists in software process improvement. Results: We have identified 32 causes, 16 effects, and 29 conducts related to resistance to change. For instance, resistant senior management can sabotage the process by creating resistance to other stakeholders who want to implement improvements. Conclusion: We found that resistance to change in software process improvement initiatives also includes resistance to follow the processes. A significant part of the conducts comprises preventive and low-cost actions. Additionally, the results pointed out situations in which given conduct to mitigate resistance can generate other positive impacts like collaborative work fostering continuous improvements. Also, we observed that individuals are more affected by resistance to change than the projects. Our findings and their interconnections expose a diverse set of circumstances that can be explored to help achieve the objectives of a software process improvement initiative.

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Cited By

Bode JKühl NKreuzberger DHoltmann C(2024)Toward Avoiding the Data Mess: Industry Insights From Data Mesh ImplementationsIEEE Access10.1109/ACCESS.2024.341729112(95402-95416)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417291
Lo D(2023)Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE)10.1109/ICSE-FoSE59343.2023.00010(69-85)Online publication date: 14-May-2023
https://doi.org/10.1109/ICSE-FoSE59343.2023.00010

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Published In

cover image ACM Other conferences

SBQS '20: Proceedings of the XIX Brazilian Symposium on Software Quality

December 2020

430 pages

ISBN:9781450389235

DOI:10.1145/3439961

Editors:
Ivan Machado
(UFBA)
,
Heitor Costa
(UFLA)
,
Adriano Albuquerque
(UNIFOR)
,
Suzana Sampaio
(UFRPE)
,
Rodrigo Santos
(UNIRIO)
,
Sheila Reinehr
(PUCPR)
,
Gleison Santos
(UNIRIO)
,
Sandro Bezerra
(UFPA)
,
Davi Viana
(UFMA)
,
Adriano Bessa Albuquerque
(UNIFOR)
,
Ana Regina Cavalcanti da Rocha
(COPPE/UFRJ)
,
Gleison Santos
(UNIRIO)
,
Ivan Machado
(UFBA)
,
Marcello Thiry
(UNIVALI)
,
Monalessa Perini Barcellos
(UFES)
,
Sheila Reinehr
(PUCPR)
,
Tayana Conte
(UFAM)
,
Davi Viana
(UFMA)
,
Marcelo Schots
(UERJ)

Copyright © 2020 ACM.

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Published: 06 March 2021

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SBQS'20

SBQS'20: 19th Brazilian Symposium on Software Quality

December 1 - 4, 2020

São Luís, Brazil

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Cited By

Bode JKühl NKreuzberger DHoltmann C(2024)Toward Avoiding the Data Mess: Industry Insights From Data Mesh ImplementationsIEEE Access10.1109/ACCESS.2024.341729112(95402-95416)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417291
Lo D(2023)Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE)10.1109/ICSE-FoSE59343.2023.00010(69-85)Online publication date: 14-May-2023
https://doi.org/10.1109/ICSE-FoSE59343.2023.00010

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