Be a Partner, not a Bystander in Software Engineering Practice: Bridging the Gaps between Academia and Industry
- URL: http://arxiv.org/abs/2604.16315v1
- Date: Sun, 08 Feb 2026 02:56:01 GMT
- Title: Be a Partner, not a Bystander in Software Engineering Practice: Bridging the Gaps between Academia and Industry
- Abstract summary: We argue that the software engineering community is deeply concerned about its research impact and relevance to industry practices.<n>We propose new calls for action and reforms in SE, and thus envision a new future for the software engineering community.
- Score: 1.6818254855155208
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Software engineering conferences bring together thousands of academicians and software practitioners so that academic research and professional practices can influence each other. In essence, a symbiotic relationship exists between the research community and the software industry, which must be maintained, nurtured and re-examined periodically. Given the major AI breakthroughs (e.g., LLMs) and large-scale adoption of AI by the software industry, a re-examination of the relationship between academia and the SE industry is highly warranted. In this position paper, we argue that the software engineering community is deeply concerned about its research impact and relevance to industry practices. By conducting an empirical study using the survey responses from the SE community, we not only provide compelling evidence supporting our position but also propose new calls for action and reforms in SE, and thus envision a new future for the software engineering community.
Related papers
- Ethics of Care for Software Engineering [51.80521257060489]
We argue that the opposition of "caring about" and "caring for" is based on the ethics of care.<n>We advocate for the adoption of ethics of care in software engineering.
arXiv Detail & Related papers (2026-01-23T20:16:00Z) - Aligning Academia with Industry: An Empirical Study of Industrial Needs and Academic Capabilities in AI-Driven Software Engineering [45.09204791294318]
The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE)<n>While top-tier SE venues continue to show sustained or emerging focus on areas like automated testing and program repair, the alignment of these academic advances with real industrial needs remains unclear.<n>This study aims to refocus academic attention on these important yet under-explored problems and to guide future SE research toward greater industrial impact.
arXiv Detail & Related papers (2025-12-17T07:29:18Z) - Quantum Artificial Intelligence for Software Engineering: the Road Ahead [9.447783914028262]
This paper presents a roadmap towards the application of Quantum AI (QAI) in software engineering.<n>We consider two of the main categories of QAI, i.e., quantum optimization algorithms and quantum machine learning.<n>We provide an overview of some of the possible challenges that need to be addressed to make the application of QAI for software engineering successful.
arXiv Detail & Related papers (2025-05-07T20:47:18Z) - Expectations vs Reality -- A Secondary Study on AI Adoption in Software Testing [0.7812210699650152]
In the software industry, artificial intelligence (AI) has been utilized more and more in software development activities.<n>In this paper, the objective was to identify what kind of empirical research with industry context has been conducted on AI in software testing.
arXiv Detail & Related papers (2025-04-07T11:03:54Z) - Generative AI and Empirical Software Engineering: A Paradigm Shift [9.284024538100063]
This vision paper examines how the integration of large language models into software engineering disrupts established research paradigms.<n>We discuss how it transforms the phenomena we study, the methods and theories we rely on, the data we analyze, and the threats to validity that arise in dynamic AI-mediated environments.<n>Our aim is to help the empirical software engineering community adapt its questions, instruments, and validation standards to a future in which AI systems are not merely tools, but active collaborators shaping software engineering and its study.
arXiv Detail & Related papers (2025-02-12T04:13:07Z) - Bridging the Quantum Divide: Aligning Academic and Industry Goals in Software Engineering [7.856941186056147]
This position paper examines the substantial divide between academia and industry within quantum software engineering.<n>This disconnect mainly arises due to academia's limited access to industry practices and the competitive nature of quantum development in commercial settings.<n>We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide.
arXiv Detail & Related papers (2025-02-10T20:17:41Z) - Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)<n>We identify benefits, harms, and future research directions for the four main themes.<n>We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Towards machine learning guided by best practices [0.0]
Machine learning (ML) is being used in software systems with multiple application fields, from medicine to software engineering (SE)
This thesis aims to answer research questions that help to understand the practices used and discussed by practitioners and researchers in the SE community.
arXiv Detail & Related papers (2023-04-29T10:58:37Z) - The Technological Emergence of AutoML: A Survey of Performant Software
and Applications in the Context of Industry [72.10607978091492]
Automated/Autonomous Machine Learning (AutoML/AutonoML) is a relatively young field.
This review makes two primary contributions to knowledge around this topic.
It provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial.
arXiv Detail & Related papers (2022-11-08T10:42:08Z) - 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) - An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago.
We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
arXiv Detail & Related papers (2022-03-08T15:46:52Z) - 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) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
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.