The Case for Vibe Modeling: A Missing Step in AI-Based Trustworthy Software Development

• URL: http://arxiv.org/abs/2607.27923v1
• Date: Thu, 30 Jul 2026 09:37:02 GMT
• Title: The Case for Vibe Modeling: A Missing Step in AI-Based Trustworthy Software Development
• Abstract summary: We argue that current AI-based development practices focus too heavily on the direct generation of code.<n>We argue for vibe modeling as a lightweight intermediate abstraction between natural language interaction and code generation.
• Score: 0.7783262415147654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly used to generate software artifacts from natural language prompts. While this enables rapid prototyping and lowers the barrier to software creation, it also introduces challenges related to understanding, validation, traceability, and trust. In this paper, we argue that current AI-based development practices focus too heavily on the direct generation of code and insufficiently on intermediate representations that preserve human intent and support reasoning about system behavior. We argue for vibe modeling as a lightweight intermediate abstraction between natural language interaction and code generation. To explore its potential, we present a student survey study that examines perceptions of LLM output understanding, validation effort, trust and the perceived usefulness of vibe modeling across several AI-assisted development scenarios. Our results are intended to inform future studies for trustworthy and explainable AI-based software engineering via vibe modeling.

Related papers

• The Vibe-Check Protocol: Quantifying Cognitive Offloading in AI Programming [5.584060970507507]
  Vibe Coding'' is a paradigm where developers articulate high-level intent through natural language and delegate implementation to AI agents.<n>This paper proposes a theoretical framework to investigate the research question: textitIs Vibe Coding a better way to learn software engineering?
  arXiv  Detail & Related papers  (2026-01-02T06:13:41Z)
• A Survey of Vibe Coding with Large Language Models [93.88284590533242]
  "Vibe Coding" is a development methodology where developers validate AI-generated implementations through outcome observation.<n>Despite its transformative potential, the effectiveness of this emergent paradigm remains under-explored.<n>This survey provides the first comprehensive and systematic review of Vibe Coding with large language models.
  arXiv  Detail & Related papers  (2025-10-14T11:26:56Z)
• Vibe Coding: Toward an AI-Native Paradigm for Semantic and Intent-Driven Programming [0.0]
  This paper introduces vibe coding, an emerging AI-native programming paradigm in which a developer specifies high-level functional intent along with qualitative descriptors of the desired "vibe"<n>An intelligent agent then transforms those specifications into executable software.
  arXiv  Detail & Related papers  (2025-10-09T22:31:53Z)
• Continual Learning for Generative AI: From LLMs to MLLMs and Beyond [56.29231194002407]
  We present a comprehensive survey of continual learning methods for mainstream generative AI models.<n>We categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based.<n>We analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones.
  arXiv  Detail & Related papers  (2025-06-16T02:27:25Z)
• Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI [0.36868085124383626]
  Review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding.<n> Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational interaction.<n>Agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention.
  arXiv  Detail & Related papers  (2025-05-26T03:00:21Z)
• SENAI: Towards Software Engineering Native Generative Artificial Intelligence [3.915435754274075]
  This paper argues for the integration of Software Engineering knowledge into Large Language Models.<n>The aim is to propose a new direction where LLMs can move beyond mere functional accuracy to perform generative tasks.<n>Software engineering native generative models will not only overcome the shortcomings present in current models but also pave the way for the next generation of generative models capable of handling real-world software engineering.
  arXiv  Detail & Related papers  (2025-03-19T15:02:07Z)
• 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)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models [50.259006481656094]
  We present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models. Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer. We present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.
  arXiv  Detail & Related papers  (2024-04-03T23:57:34Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model [74.4875156387271]
  We develop a novel foundation model pre-trained with huge multimodal (visual and textual) data. We show that state-of-the-art results can be obtained on a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2021-10-27T12:25:21Z)

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.