Bridging the Gap: Adapting Evidence to Decision Frameworks to support the link between Software Engineering academia and industry

• URL: http://arxiv.org/abs/2602.08015v1
• Date: Sun, 08 Feb 2026 15:30:19 GMT
• Title: Bridging the Gap: Adapting Evidence to Decision Frameworks to support the link between Software Engineering academia and industry
• Authors: Patricia G. F. Matsubara, Tayana Conte,
• Abstract summary: We introduce Evidence to Decision (EtD) frameworks from the health sciences.<n>EtD frameworks propose gathering experts in panels to assess the existing best evidence about the impact of an intervention.<n>We discuss the challenges the SE research and practice community may face when adopting EtD frameworks.
• Score: 4.140124714399536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.

Related papers

• Let the Barbarians In: How AI Can Accelerate Systems Performance Research [80.43506848683633]
  We term this iterative cycle of generation, evaluation, and refinement AI-Driven Research for Systems.<n>We demonstrate that ADRS-generated solutions can match or even outperform human state-of-the-art designs.
  arXiv  Detail & Related papers  (2025-12-16T18:51:23Z)
• A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives [29.526174878343742]
  We provide a systematic review of industrial recommender systems and contrast them with their academic counterparts.<n>We highlight key differences in data scale, real-time requirements, and evaluation methodologies.<n>We then examine how industry practitioners address these challenges in Transaction-Oriented Recommender Systems and Content-Oriented Recommender Systems.
  arXiv  Detail & Related papers  (2025-09-07T10:29:41Z)
• LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios [63.08653028889316]
  We propose a systematic taxonomy that decomposes agentic reasoning frameworks and analyze how these frameworks dominate framework-level reasoning.<n>Specifically, we propose an unified formal language to further classify agentic reasoning systems into single-agent methods, tool-based methods, and multi-agent methods.<n>We provide a comprehensive review of their key application scenarios in scientific discovery, healthcare, software engineering, social simulation, and economics.
  arXiv  Detail & Related papers  (2025-08-25T06:01:16Z)
• Can Large Language Models Match the Conclusions of Systematic Reviews? [43.27182445778988]
  We ask: Can large language models (LLMs) match the conclusions of systematic reviews written by clinical experts when given access to the same studies?<n>We benchmark 24 LLMs on MedEvidence, including reasoning, non-reasoning, medical specialist, and models across varying sizes (from 7B-700B).<n>We find that reasoning does not necessarily improve performance, larger models do not consistently yield greater gains, and knowledge-based fine-tuning degrades accuracy on MedEvidence.
  arXiv  Detail & Related papers  (2025-05-28T18:58:09Z)
• A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice [12.047370735494601]
  Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions.<n>This survey reviews the progress in RS inclusively from 2017 to 2024.<n>The primary goal of this survey is to bridge theory with practice.
  arXiv  Detail & Related papers  (2024-07-18T17:00:53Z)
• Teaching Design Science as a Method for Effective Research Development [0.24578723416255752]
  Applying Design Science Research (DSR) methodology is becoming a popular working resource for Information Systems (IS) and Software engineering studies. This chapter includes examples of DSR, a teaching methodology, learning objectives, and recommendations. We have created a survey artifact intended to gather data on the experiences of design science users.
  arXiv  Detail & Related papers  (2024-07-13T10:43:06Z)
• LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework synergizes open-world knowledge with collaborative knowledge.<n>We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis [91.5632751731927]
  Large Language Models such as ChatGPT have showcased remarkable abilities in solving general tasks.<n>We propose a general framework for utilizing LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders.<n>We analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results.
  arXiv  Detail & Related papers  (2024-01-10T08:28:56Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Enterprise Architecture in Healthcare Systems: A systematic literature review [0.0]
  Enterprise architecture (EA) has been present in scientific literature since the 1980s. EA delivers value by presenting business and ICT leaders with recommendations for adjusting policies and projects to achieve business goals. This work presents a systematic literature review to select studies demonstrating current EA practices in healthcare systems.
  arXiv  Detail & Related papers  (2020-07-14T02:01:25Z)
• Recommender Systems for the Internet of Things: A Survey [53.865011795953706]
  Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things. Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data.
  arXiv  Detail & Related papers  (2020-07-14T01:24:44Z)

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.