Industry-academia research collaboration and knowledge co-creation: Patterns and anti-patterns
April 29, 2022 Β· Declared Dead Β· π ACM Transactions on Software Engineering and Methodology
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dusica Marijan, Sagar Sen
arXiv ID
2204.14180
Category
cs.SE: Software Engineering
Cross-listed
cs.LG
Citations
31
Venue
ACM Transactions on Software Engineering and Methodology
Last Checked
4 months ago
Abstract
Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community. The problem of two cultures, research conducted in a vacuum (disconnected from the real world), or misaligned time horizons are just some of the many complex challenges standing in the way of successful industry-academia collaborations. This paper reports on the experience of research collaboration and knowledge co-creation between industry and academia in software engineering as a way to bridge the research-practice collaboration gap. Our experience spans 14 years of collaboration between researchers in software engineering and the European and Norwegian software and IT industry. Using the participant observation and interview methods we have collected and afterwards analyzed an extensive record of qualitative data. Drawing upon the findings made and the experience gained, we provide a set of 14 patterns and 14 anti-patterns for industry-academia collaborations, aimed to support other researchers and practitioners in establishing and running research collaboration projects in software engineering.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted