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Industry-academia collaboration for realism in software engineering research : Insights and recommendations

Song, Qunying LU orcid and Runeson, Per LU orcid (2023) In Information and Software Technology 156.
Abstract

Context: Effective industry-academia collaboration may increase software engineering research relevance by increased realism, yet very challenging for reasons like confidentiality concerns, different objectives and priorities. Objective: We analyse industry-academia collaboration scenarios based on our own experiences as Ph.D. student and supervisor, and provide insights and recommendations to facilitate future collaborations with industry. Method: We first present our industry-academia collaboration experiences that span over two and a half years with different companies. Then, we analyse both facilitators and problems from those scenarios and synthesize recommendations based on that. Results: Five different scenarios are analysed,... (More)

Context: Effective industry-academia collaboration may increase software engineering research relevance by increased realism, yet very challenging for reasons like confidentiality concerns, different objectives and priorities. Objective: We analyse industry-academia collaboration scenarios based on our own experiences as Ph.D. student and supervisor, and provide insights and recommendations to facilitate future collaborations with industry. Method: We first present our industry-academia collaboration experiences that span over two and a half years with different companies. Then, we analyse both facilitators and problems from those scenarios and synthesize recommendations based on that. Results: Five different scenarios are analysed, including both success and failure scenarios. Reflections and insights into these experiences as well as some general recommendations are presented. Conclusion: We believe such experiences and insights are helpful for academic researchers to pursue industry-academia collaboration. We plan to continuously report our experience and provide our suggestions for effective collaboration with industry.

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Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Industry-academia collaboration, Software engineering
in
Information and Software Technology
volume
156
article number
107135
publisher
Elsevier
external identifiers
  • scopus:85144604352
ISSN
0950-5849
DOI
10.1016/j.infsof.2022.107135
project
Software testing of autonomous systems
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2022 The Author(s)
id
c26c37db-25b6-4ec1-b356-4749c031ac72
date added to LUP
2023-01-09 08:09:30
date last changed
2024-06-13 14:31:33
@article{c26c37db-25b6-4ec1-b356-4749c031ac72,
  abstract     = {{<p>Context: Effective industry-academia collaboration may increase software engineering research relevance by increased realism, yet very challenging for reasons like confidentiality concerns, different objectives and priorities. Objective: We analyse industry-academia collaboration scenarios based on our own experiences as Ph.D. student and supervisor, and provide insights and recommendations to facilitate future collaborations with industry. Method: We first present our industry-academia collaboration experiences that span over two and a half years with different companies. Then, we analyse both facilitators and problems from those scenarios and synthesize recommendations based on that. Results: Five different scenarios are analysed, including both success and failure scenarios. Reflections and insights into these experiences as well as some general recommendations are presented. Conclusion: We believe such experiences and insights are helpful for academic researchers to pursue industry-academia collaboration. We plan to continuously report our experience and provide our suggestions for effective collaboration with industry.</p>}},
  author       = {{Song, Qunying and Runeson, Per}},
  issn         = {{0950-5849}},
  keywords     = {{Industry-academia collaboration; Software engineering}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Information and Software Technology}},
  title        = {{Industry-academia collaboration for realism in software engineering research : Insights and recommendations}},
  url          = {{http://dx.doi.org/10.1016/j.infsof.2022.107135}},
  doi          = {{10.1016/j.infsof.2022.107135}},
  volume       = {{156}},
  year         = {{2023}},
}