Industry-Academia Research Collaboration in Software Engineering: The Certus Model

• URL: http://arxiv.org/abs/2204.11039v1
• Date: Sat, 23 Apr 2022 10:16:23 GMT
• Title: Industry-Academia Research Collaboration in Software Engineering: The Certus Model
• Authors: Dusica Marijan, Arnaud Gotlieb
• Abstract summary: Building scalable and effective research collaborations in software engineering is known to be challenging. This paper aims to understand what are the elements of a successful industry-academia collaboration that enable the culture of participative knowledge creation.
• Score: 13.021014899410684
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Context: Research collaborations between software engineering industry and academia can provide significant benefits to both sides, including improved innovation capacity for industry, and real-world environment for motivating and validating research ideas. However, building scalable and effective research collaborations in software engineering is known to be challenging. While such challenges can be varied and many, in this paper we focus on the challenges of achieving participative knowledge creation supported by active dialog between industry and academia and continuous commitment to joint problem solving. Objective: This paper aims to understand what are the elements of a successful industry-academia collaboration that enable the culture of participative knowledge creation. Method: We conducted participant observation collecting qualitative data spanning 8 years of collaborative research between a software engineering research group on software V&V and the Norwegian IT sector. The collected data was analyzed and synthesized into a practical collaboration model, named the Certus Model. Results: The model is structured in seven phases, describing activities from setting up research projects to the exploitation of research results. As such, the Certus model advances other collaborations models from literature by delineating different phases covering the complete life cycle of participative research knowledge creation. Conclusion: The Certus model describes the elements of a research collaboration process between researchers and practitioners in software engineering, grounded on the principles of research knowledge co-creation and continuous commitment to joint problem solving. The model can be applied and tested in other contexts where it may be adapted to the local context through experimentation.

Related papers

• Agile Minds, Innovative Solutions, and Industry-Academia Collaboration: Lean R&D Meets Problem-Based Learning in Software Engineering Education [1.4454625330080995]
  This paper aims to extend Lean R&D with skill principles, emphasizing business and software development synergy. The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems. Students reported increased knowledge proficiency and perceived working on real problems as contributing the most to their learning.
  arXiv  Detail & Related papers  (2024-07-22T18:47:14Z)
• Teaching Research Design in Software Engineering [1.9659095632676098]
  Empirical Software Engineering (ESE) has emerged as a contending force aiming to critically evaluate and provide knowledge that informs practice in adopting new technologies. This chapter teaches foundational skills in research design, essential for educating software engineers and researchers in ESE.
  arXiv  Detail & Related papers  (2024-07-06T21:06:13Z)
• Action Research with Industrial Software Engineering -- An Educational Perspective [0.5257115841810258]
  Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings. It requires a different kind of interaction with the software development organisation. This chapter is intended to support learning and teaching action research.
  arXiv  Detail & Related papers  (2024-07-05T17:05:06Z)
• MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
  We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects. MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
  arXiv  Detail & Related papers  (2024-06-10T15:19:09Z)
• SciOps: Achieving Productivity and Reliability in Data-Intensive Research [0.8414742293641504]
  Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals. Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, inspiration and automation. We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations.
  arXiv  Detail & Related papers  (2023-12-29T21:37:22Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Towards Automated Process Planning and Mining [77.34726150561087]
  We present a research project in which researchers from the AI and BPM field work jointly together. We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
  arXiv  Detail & Related papers  (2022-08-18T16:41:22Z)
• Industry-academia research collaboration and knowledge co-creation: Patterns and anti-patterns [12.434688497507397]
  This paper reports on the experience of research collaboration and knowledge co-creation between industry and academia in software engineering. Our experience spans 14 years of collaboration between researchers in software engineering and the European and Norwegian software and IT industry. Drawing upon the findings made and the experience gained, we provide a set of 14 patterns and 14 anti-patterns for industry-academia collaborations.
  arXiv  Detail & Related papers  (2022-04-29T16:10:19Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA [57.06643156253045]
  Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines. We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA. FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research.
  arXiv  Detail & Related papers  (2020-11-09T21:23:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.