# Studies about: what are best practices in vibe coding

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## 1. Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook – a Grey Literature Review
2026 · Proceedings of the IEEE/ACM 48th International Conference on Software Engineering: Software Engineering in Practice · Ahmed Fawzy; Amjed Tahir; Kelly Blincoe · cited 3 times · conference-paper · open access
Link: https://doi.org/10.1145/3786583.3786866 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3786583.3786866

Vibe coding is the practice where coders rely on AI code generation tools through intuition and trial-and-error without necessarily understanding the underlying code. Despite widespread adoption, there has been no systematic investigation into why vibe coders engage in vibe coding, what they experience while doing so, and how they approach quality assurance (QA) and perceive the quality of AI-generated code. To this end, we conduct a systematic grey literature review of 101 practitioner sources, extracting 518 firsthand behavioral accounts about vibe coding practices, challenges, and limitations. Our analysis reveals a speed–quality trade-off paradox, where vibe coders report being motivated by speed and accessibility, often experiencing rapid “instant success and flow”, yet most perceive the resulting code as fast but flawed. Our analysis suggests that QA practices are frequently overlooked, with many reporting skipping testing, relying on the models’ or tools’ outputs without modification, or delegating checks back to the AI code generation tools. While vibe coding lowers barriers and accelerates prototyping, it can introduce reliability and maintainability concerns. These insights carry implications for tool designers and software development teams. Understanding how vibe coding is practiced today is crucial for guiding its responsible use and preventing a broader QA crisis in AI-assisted development.

## 2. Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification
2025 · Proceedings of the 1st ACM SIGPLAN International Workshop on Language Models and Programming Languages · Jacqueline Mitchell; Yasser Shaaban · cited 6 times · conference-paper · open access
Link: https://doi.org/10.1145/3759425.3763390 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3759425.3763390

“Vibe coding” — the practice of developing software through iteratively conversing with a large language model (LLM) — has exploded in popularity within the last year. However, developers report key limitations including the accumulation of technical debt, security issues, and code churn to achieve satisfactory results. We argue that these pitfalls result from LLMs’ inability to reconcile accumulating human-imposed constraints during vibe coding, with developers inadvertently failing to resolve contradictions because LLMs prioritize user commands over code consistency. Given LLMs’ receptiveness to verification-based feedback, we argue that formal methods can mitigate these pitfalls, making vibe coding more reliable. However, we posit that integrating formal methods must transcend existing approaches that combine formal methods and LLMs. We advocate for a side-car system throughout the vibe coding process which: (1) Autoformalizes specifications (2) Validates against targets, (3) Delivers actionable feedback to the LLM, and (4) Allows intuitive developer influence on specifications.

## 3. Generative AI Vibe Coding for Prototyping Industrial Systems
2025 · 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE) · Daswin De Silva; Nishan Mills; Zafar Issadeen; and 3 more · cited 7 times · conference-paper
Link: https://doi.org/10.1109/isie62713.2025.11124737 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1109/isie62713.2025.11124737

Vibe coding is a recent Generative AI innovation where a human operator provides conversational natural language description of a problem and an AI assistant writes the code to develop a solution. It is more than collaborating or assisting with coding, as the entirety of coding and technical implementation is delegated to the AI agent. Vibe coding presents an immediate productivity boost for prototype development, as it addresses the growing complexity of development tools, increased demand for time-efficient prototype development, and the shortage of skills needed to address this demand. This paper presents the role and contribution of vibe coding for prototyping industrial systems. The paper begins with an introduction to vibe coding and vibe coding tools, followed by a workflow comparison of conventional prototyping and vibe coding worked through a sample scenario of an industrial system prototype. A further productivity comparison of the two approaches across diverse dimensions of cognitive load, development speed, code quality, debugging and iteration is also reported. The paper concludes by highlighting the opportunities and risks of vibe coding, and a selection of best practices that can accelerate the adoption of vibe coding for prototyping industrial systems.

## 4. Vibe coding: programming through conversation with artificial intelligence
2025 · arXiv (Cornell University) · Advait Sarkar; Ian Drosos · cited 9 times · preprint · open access
Link: https://doi.org/10.48550/arxiv.2506.23253 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.48550/arxiv.2506.23253

We examine "vibe coding": an emerging programming paradigm where developers primarily write code by interacting with code-generating large language models rather than writing code directly. We present the first empirical study of vibe coding. We analysed over 8 hours of curated video capturing extended vibe coding sessions with rich think-aloud reflections. Using framework analysis, we investigated programmers' goals, workflows, prompting techniques, debugging approaches, and challenges encountered. We find that vibe coding follows iterative goal satisfaction cycles where developers alternate between prompting AI, evaluating generated code through rapid scanning and application testing, and manual editing. Prompts in vibe coding blend vague, high-level directives with detailed technical specifications. Debugging remains a hybrid process combining AI assistance with manual practices. Critically, vibe coding does not eliminate the need for programming expertise but rather redistributes it toward context management, rapid code evaluation, and decisions about when to transition between AI-driven and manual manipulation of code. Trust in AI tools during vibe coding is dynamic and contextual, developed through iterative verification rather than blanket acceptance. Vibe coding is an evolution of AI-assisted programming that represents an early manifestation of "material disengagement", wherein practitioners orchestrate code production and manipulation, mediated through AI, while maintaining selective and strategic oversight.

## 5. Responsible Vibe Coding: Architecture, Opportunities, and Research Agenda
2026 · Journal of Computer Information Systems · Ibrahim A. Elgendy; Yogesh K. Dwivedi; Mohammed Abdullah Al-Sharafi; and 8 more · cited 6 times · article · open access
Link: https://doi.org/10.1080/08874417.2026.2621186 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1080/08874417.2026.2621186

Vibe coding is an emerging software engineering approach that transitions development from manual code writing to expressing conversational intent. Building on recent advances in generative artificial intelligence (AI) and large language models (LLMs), this approach enables developers to experiment with specifying and generating software artifacts through natural-language interaction. This paper defines vibe coding as a socio-technical framework that reimagines human–AI collaboration across the development lifecycle. Drawing on recent research and emerging industry, it explores the architectural fundamentals of vibe coding, compares it with related concepts such as agentic development and AI-assisted coding, and highlights its proposed applications while illustrating its potential through several illustrative scenarios. The paper also discusses key challenges in reliability, governance, and security, proposing a structured research agenda focused on human-centered adaptation, technological reliability, and organizational governance. By examining vibe coding as a socio-technical phenomenon, this study provides a foundational perspective on responsible, intent-driven software creation.

## 6. Vibe Coding in Practice: Building a Driving Simulator Without Expert Programming Skills
2025 · Adjunct Proceedings of the 17th International Conference on Automotive User Interfaces and Interactive Vehicular Applications · Margarida Fortes-Ferreira; Md Shadab Alam; Pavlo Bazilinskyy · cited 7 times · conference-paper · open access
Link: https://doi.org/10.1145/3744335.3758482 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3744335.3758482

The emergence of large language models has introduced new opportunities in software development, particularly through a revolutionary paradigm known as vibe coding or "coding by vibes", in which developers express their software ideas in natural language and where the LLM generates the code.This paper investigates the potential of vibe coding to support novice programmers.The first author, without coding experience, attempted to create a 3D driving simulator using the Cursor platform and Three.js.The iterative prompting process improved the simulation's functionality and visual quality.The results indicated that LLM can reduce barriers to creative development and expand access to computational tools.However, challenges remain: prompts often required refinements, output code can be logically flawed, and debugging demanded a foundational understanding of programming concepts.These findings highlight that while vibe coding increases accessibility, it does not completely eliminate the need for technical reasoning and understanding prompt engineering.

## 7. VIBE CODING AND TECHNICALDEBT: A MULTIVOCAL LITERATUREREVIEW AND MIXED-METHODSSTUDY
2026 · DiVA (Mälardalen University College) · Victor Mertala; Vasilije Ivanisevic · dissertation · open access
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-77343 · on the map of science, with its citations: https://sciencemadereadable.com/?work=W7165435575

The rapid adoption of large language models (LLMs) such as ChatGPT and GitHub Copilot has introduced new AI-assisted development practices in software engineering. One emerging approach, commonly referred to as vibe coding, involves generating source code directly from natural-language descriptions, often reducing or bypassing traditional software engineering activities such as detailed design and architectural planning. While this approach can improve development speed and productivity, its implications for software quality and long-term maintainability remain insufficiently understood. This thesis investigates the relationship between vibe coding and technical debt, a concept describing the long-term consequences of suboptimal design and implementation decisions. The main objective is to analyze whether and how vibe coding contributes to the accumulation of technical debt, identify the types of debt most commonly associated with such practices, and explore developers' perceptions of related benefits and drawbacks. To address these goals, a mixed-methods approach is adopted, combining a multivocal literature review with an empirical study. The multivocal literature review synthesizes academic and practitioner-oriented sources on vibe coding and technical debt, while the empirical component collects and analyzes data from developers through a survey and follow-up interviews with practitioners. The findings suggest that vibe coding is associated with several technical debt risks, particularly code debt, architectural debt, and maintainability-related debt. Reported issues include duplicated logic, hidden bugs, insufficient validation, reduced code understanding, and generated solutions that may work locally but conflict with the broader system architecture. At the same time, participants reported benefits such as faster development, reduced manual effort, and rapid prototyping. Overall, the results indicate a trade-off between short-term productivity and long-term software quality, suggesting that vibe coding can be useful when combined with human oversight, testing, refactoring, and architectural awareness, but may contribute to technical debt when generated code is accepted without sufficient review or understanding.

## 8. A Survey of Vibe Coding with Large Language Models
2025 · arXiv (Cornell University) · Y.T. Ge; Lingrui Mei; Duan, Zenghao; and 12 more · cited 4 times · preprint · open access
Link: https://doi.org/10.48550/arxiv.2510.12399 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.48550/arxiv.2510.12399

The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding agents, enabling a novel development methodology termed "Vibe Coding" where developers validate AI-generated implementations through outcome observation rather than line-by-line code comprehension. Despite its transformative potential, the effectiveness of this emergent paradigm remains under-explored, with empirical evidence revealing unexpected productivity losses and fundamental challenges in human-AI collaboration. To address this gap, this survey provides the first comprehensive and systematic review of Vibe Coding with large language models, establishing both theoretical foundations and practical frameworks for this transformative development approach. Drawing from systematic analysis of over 1000 research papers, we survey the entire vibe coding ecosystem, examining critical infrastructure components including LLMs for coding, LLM-based coding agent, development environment of coding agent, and feedback mechanisms. We first introduce Vibe Coding as a formal discipline by formalizing it through a Constrained Markov Decision Process that captures the dynamic triadic relationship among human developers, software projects, and coding agents. Building upon this theoretical foundation, we then synthesize existing practices into five distinct development models: Unconstrained Automation, Iterative Conversational Collaboration, Planning-Driven, Test-Driven, and Context-Enhanced Models, thus providing the first comprehensive taxonomy in this domain. Critically, our analysis reveals that successful Vibe Coding depends not merely on agent capabilities but on systematic context engineering, well-established development environments, and human-agent collaborative development models.

## 9. Transforming web development with NLP and vibe coding techniques
2025 · Aaltodoc (Aalto University) · Heinonen, Joona · dissertation · open access
Link: https://aaltodoc.aalto.fi/handle/123456789/139018 · on the map of science, with its citations: https://sciencemadereadable.com/?work=W7083216749

With LLMs and AI getting significantly better in recent years, it has changed how web development is approached. This thesis aims to cover the changes that vibe coding and natural language programming bring to web development and what type of benefits and drawbacks might emerge. Vibe coding and fully delegating development to AI tools is a relatively new area in web development, which is the reason for this study. This study is conducted as a literature review going over papers and research related to vibe coding and no-code style programming in web development. Web development has shifted from manually writing code to generating code and tests with AI agents, which leaves resources for other parts of the process. Delegating development to AI has several levels depending on the amount of work left to the human developer, ranging from expert consultant to full delegation - in other words, using vibe coding. Vibe coding is defined as leaving everything to the "vibes" referring to fully letting the AI agent create and test the web application. Vibe coding can bring benefits to the user with its speed and knowledge, offering an easy way to try new projects and ideas with limited resources. it can also teach users about certain algorithms and architectural decisions. Applying vibe coding can also bring a series of drawbacks, such as vulnerabilities in the form of security issues and risks. The use of LLMs can also bring ethical issues, such as bias in the LLM outputs, LLM hallucinations in the form of non-factual outputs, and the non-transparent use of AI. Furthermore, purely using AI to program can lead to the decay of knowledge for the developer, leading to more risks in the future.

## 10. Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
2025 · arXiv (Cornell University) · Veronica Pimenova; Sarah Fakhoury; Christian Bird; and 2 more · cited 5 times · preprint · open access
Link: https://doi.org/10.48550/arxiv.2509.12491 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.48550/arxiv.2509.12491

Vibe coding, a term coined by Andrej Karpathy in February 2025, has quickly become a compelling and controversial natural language programming paradigm in AI-assisted software development. Centered on iterative co-design with an AI assistant, vibe coding emphasizes flow and experimentation over strict upfront specification. While initial studies have begun to explore this paradigm, most focus on analyzing code artifacts or proposing theories with limited empirical backing. There remains a need for a grounded understanding of vibe coding as it is perceived and experienced by developers. We present the first systematic qualitative investigation of vibe coding perceptions and practice. Drawing on over 190,000 words from semi-structured interviews, Reddit threads, and LinkedIn posts, we characterize what vibe coding is, why and how developers use it, where it breaks down, and which emerging practices aim to support it. We propose a qualitatively grounded theory of vibe coding centered on conversational interaction with AI, co-creation, and developer flow and joy. We find that AI trust regulates movement along a continuum from delegation to co-creation and supports the developer experience by sustaining flow. We surface recurring pain points and risks in areas including specification, reliability, debugging, latency, code review burden, and collaboration. We also present best practices that have been discovered and shared to mitigate these challenges. We conclude with implications for the future of AI dev tools and directions for researchers investigating vibe coding.

## 11. Vibe Modeling: Challenges and Opportunities
2025 · Lecture notes in computer science · Jordi Cabot · cited 5 times · conference-paper
Link: https://doi.org/10.1007/978-3-032-08620-4_7 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1007/978-3-032-08620-4_7

There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems. New types of user interfaces, the need for intelligent components, sustainability concerns, ... bring new challenges that we need to handle. In the last years, model-driven engineering (MDE) has been key to improving the quality and productivity of software development, but models themselves are becoming increasingly complex to specify and manage. At the same time, we are witnessing the growing popularity of vibe coding approaches that rely on Large Language Models (LLMs) to transform natural language descriptions into running code at the expenses of code vulnerabilities, scalability issues and maintainability concerns. In this paper, we introduce the concept of \textit{vibe modeling} as a novel approach to integrate the best of both worlds (AI and MDE) to speed up the development of reliable complex systems. We outline the key concepts of vibe modeling and highlight the opportunities and open challenges it presents for the future of modeling.

## 12. Building Software by Rolling the Dice: A Qualitative Study of Vibe Coding
2026 · Proceedings of the ACM on software engineering. · Yi-Hung Chou; Boyuan Jiang; Yi Wen Chen; and 4 more · cited 1 times · article · open access
Link: https://doi.org/10.1145/3797105 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3797105

Large language models (LLMs) are reshaping software engineering by enabling vibe coding—building software primarily through prompts rather than writing code. Although widely publicized as a productivity breakthrough, little is known about how practitioners actually define and engage in these practices. To shed some light on this emerging phenomenon, we conducted a grounded theory study of 20 vibe-coding videos, including 7 live-streamed coding sessions (approximately 16 hours, 254 prompts) and 13 opinion videos (approximately 5 hours), supported by additional analysis of activity durations and intents of prompts. Our findings reveal a spectrum of behaviors: some vibe coders rely almost entirely on AI without inspecting code, while others examine and adapt generated outputs. Across approaches, all must contend with the stochastic nature of generation, with debugging and refinement described as “rolling the dice.” Further, divergent mental models, shaped by vibe coders’ expertise and reliance on AI, influence prompting strategies, evaluation practices, and levels of trust. Through additional quantitative analysis, vibe coders spend over 20% of session time on average waiting for model responses, with some sessions exceeding 50%. We also observe prompt redundancy: for some participants, nearly 40% of prompts repeat prior intents. These findings open new directions for research on the future of software engineering and point to practical opportunities for tool design and education.

## 13. From Vibe to Spec: An Empirical Study to Demystify Specifications in Code Generation
2026 · IEEE/ACM International Conference on Automated Software Engineering (ASE) · Ruirui He; Hanqing Lu; Lezhi Ma; and 5 more · conference-paper · open access
Link: https://doi.org/10.1145/3832783.3837515 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3832783.3837515

Recent advances in large language models (LLMs) for code generation have given rise to vibe coding, a programming practice in which developers interact with LLMs through unstructured natural language and primarily validate implementations by inspecting overall execution outputs. However, the inherent ambiguity and inconsistency of natural language present substantial challenges for generating complex and large-scale software systems. To mitigate these limitations, an emerging AI programming paradigm—specification-driven development—advocates providing LLMs with precise and consistent specifications to guide code generation. Despite its promise, fundamental questions regarding the appropriate forms of specifications and the granularity at which they should be supplied to LLMs remain largely unexplored.

## 14. Zoro: Active Controls to Support Rule Following in Vibe Coding
2026 · ACM Symposium on User Interface Software and Technology (UIST) · Jenny Ma; Sitong Wang; Joshua H. Kung; and 1 more · conference-paper · open access
Link: https://doi.org/10.1145/3830398.3830629 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3830398.3830629

Rules files (e.g., AGENTS.md, CLAUDE.md) are the primary mechanism for human-agent alignment when developers vibe code. However, they remain passive: it is not immediately apparent when rules are being used or followed, or how to improve them. To transform rules from passive text into active controls, we introduce Zoro, an interactive interface that integrates directly with a coding agent and anchors rules to every step of the coding process. After an agent generates an initial plan, Zoro enriches the plan with rules, enforces the rules during implementation by requiring the agent to prove that each rule was followed, and allows users to provide in-situ feedback when they are unsatisfied with a rule application to evolve the scope of the rules. A technical evaluation shows that coding agents follow rules more with Zoro than without. A user study demonstrates a change in people’s behavior and cognitive strategies when rules are at the forefront of vibe coding. We discuss how making rules active in agentic systems unlocks broader opportunities for human-agent alignment in coding settings and beyond.

## 15. Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency
2026 · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · Sverrir Thorgeirsson; Theo B. Weidmann; Zhendong Su · cited 2 times · conference-paper · open access
Link: https://doi.org/10.1145/3772318.3791666 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3772318.3791666

Many software development platforms now support LLM-driven programming, or “vibe coding”, a technique that allows one to specify programs in natural language and iterate from observed behavior, all without directly editing source code. While its adoption is accelerating, little is known about which skills best predict success in this workflow. We report a preregistered cross-sectional study with tertiary-level students (N = 100) who completed measures of computer-science achievement, domain-general cognitive skills, written-communication proficiency, and a vibe-coding assessment. Tasks were curated via an eight-expert consensus process and executed in a purpose-built, vibe-coding environment that mirrors commercial tools while enabling controlled evaluation. We find that both writing skill and CS achievement are significant predictors of vibe-coding performance, and that CS achievement remains a significant predictor after controlling for domain-general cognitive skills. The results may inform tool and curriculum design, including when to emphasize prompt-writing versus CS fundamentals to support future software creators.

## 16. Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda
2025 · arXiv (Cornell University) · Meske, Christian; Hermanns, Tobias; von der Weiden, Esther; and 2 more · cited 3 times · preprint · open access
Link: https://doi.org/10.48550/arxiv.2507.21928 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.48550/arxiv.2507.21928

Software development is undergoing a fundamental transformation as vibe coding becomes widespread, with large portions of contemporary codebases now being generated by Artificial Intelligence (AI). The disconnect between rapid adoption and limited conceptual understanding highlights the need for an inquiry into this emerging paradigm. Drawing on an intent perspective and historical analysis, we define vibe coding as a software development paradigm where humans and Generative AI (GenAI) engage in collaborative flow to co-create software artifacts through natural language dialogue, shifting the mediation of developer intent from deterministic instruction to probabilistic inference. By intent mediation, we refer to the fundamental process through which developers translate their conceptual goals into representations that computational systems can execute. Our results show that vibe coding redistributes epistemic labor between humans and machines, shifting expertise from technical implementation toward collaborative orchestration. We identify key opportunities, including democratization, acceleration, and systemic leverage, alongside risks such as black-box codebases, responsibility gaps, and ecosystem bias. We conclude with a research agenda spanning human-, technology-, and organization-centered directions to guide future investigations of this paradigm.

## 17. Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
2025 · arXiv (Cornell University) · Ranjan Sapkota; Konstantinos I. Roumeliotis; Manoj Karkee · cited 5 times · preprint · open access
Link: https://doi.org/10.48550/arxiv.2505.19443 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.48550/arxiv.2505.19443

This review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding. While both leverage large language models (LLMs), they differ fundamentally in autonomy, architectural design, and the role of the developer. Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational workflows that support ideation, experimentation, and creative exploration. In contrast, agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention. We propose a detailed taxonomy spanning conceptual foundations, execution models, feedback loops, safety mechanisms, debugging strategies, and real-world tool ecosystems. Through comparative workflow analysis and 20 detailed use cases, we illustrate how vibe systems thrive in early-stage prototyping and education, while agentic systems excel in enterprise-grade automation, codebase refactoring, and CI/CD integration. We further examine emerging trends in hybrid architectures, where natural language interfaces are coupled with autonomous execution pipelines. Finally, we articulate a future roadmap for agentic AI, outlining the infrastructure needed for trustworthy, explainable, and collaborative systems. Our findings suggest that successful AI software engineering will rely not on choosing one paradigm, but on harmonizing their strengths within a unified, human-centered development lifecycle.

## 18. The implemetnation of vibe coding in software engineering
2026 · Репозиторий БГУИР (BSUIR Repository) · A. V. Kungurtsev · article · open access
Link: https://libeldoc.bsuir.by/handle/123456789/64653 · on the map of science, with its citations: https://sciencemadereadable.com/?work=W7169479106

Vibe coding is an emerging AI-driven development technique characterized by a more intuitive, «by feel» approach, using natural language prompts for LLMs and involving minimal manual line-by-line programming, applied to create and improve software models. In order to understand this groundbreaking paradigm shift, in this article we examine the main points of practical implementation, effects on the labor market, and the benefits and risks of the phenomenon in question. The main objective is to dig into queries of maintainability, security, long-term technical efficiency and speed and accessibility.

## 19. A Technical Debt-Aware Prompting Framework for Sustainable Vibe Coding: Addressing the Production Readiness Crisis in AI-Assisted Software Development
2025 · Marco H.K. van Hurne · preprint · open access
Link: https://doi.org/10.36227/techrxiv.175459417.76916566/v1 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.36227/techrxiv.175459417.76916566/v1

Background: Vibe coding has gained widespread adoption among developers (% adoption rate), offering intuitive AI-assisted software development through natural language interfaces. However, current practices systematically generate technical debt, creating substantial post-deployment resource burdens through performance inefficiencies, security vulnerabilities, and maintenance overhead. Objective: This working paper presents a novel Technical Debt-Aware Prompting Framework that integrates structured prompting methodologies with comprehensive context injection to transform vibe coding from a technical debt generator into a sustainable, enterprise-grade development approach. Methods: We developed an 11-domain hierarchical framework coupled with a 41- question Context Injection Questionnaire, validated against systematic literature analysis of 50 studies examining AI-assisted code generation, security integration, and performance optimization. The framework implements cross-domain validation, intelligent prompt generation, and continuous technical debt prevention mechanisms. Current Status: The framework is currently being implemented and empirically tested across multiple use cases and organizational contexts. Preliminary Results: The framework addresses critical gaps identified in current research: structured prompting shows only 13.79% improvement over traditional methods, security-focused approaches create false trade-offs with functionality, and memory management remains underexplored in 89% of studies. Our theoretical analysis indicates the framework can resolve these limitations through integrated multi-objective optimization, production-aware generation, and proactive quality assurance. Empirical validation is ongoing. Expected Contributions: Upon completion of implementation and testing, this research will demonstrate a paradigm shift from reactive technical debt management to proactive prevention, enabling organizations to leverage AI-assisted development while maintaining production standards. The framework preserves vibe coding's accessibility while embedding enterprise-grade quality considerations throughout the development lifecycle.

## 20. FlowCheck: Helping End-Users Specify and Verify Intent in Vibe-Coded Web Apps
2026 · ACM SIGPLAN International Workshop on Language Models and Programming Languages (LMPL) · Reya Vir; Lydia Chilton; Zhuo Zhang; and 1 more · conference-paper · open access
Link: https://doi.org/10.1145/3843750.3843844 · on the map of science, with its citations: https://sciencemadereadable.com/?work=10.1145/3843750.3843844

Vibe-coded applications often contain silent behavioral failures in which the interface appears functional even though user-visible information does not flow to the expected state or output. We introduce FlowCheck, a constraint language to specify these user-visible information flows directly through the application interface, where constraints can also be displayed and inspected without reading code, and are structured enough for reliable LLM generation. FlowCheck translates the constraints into deterministic CodeQL analyses, and we evaluate it across four applications generated via Claude Code, and compare with three coding models as bug-finding baselines. We find that FlowCheck correctly translates and flags all 30 of our injected constraint violations with no false positives. In contrast, frontier models (Claude Opus 4.7, DeepSeek V3, and Gemini Pro) showed significantly lower accuracy when prompted to find bugs in the same code, with none achieving full accuracy. This approach lets vibe coders state intent in terms of the interface they understand, and checks it deterministically against the code they do not.

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