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Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review
Ahmed Fawzy, Amjed Tahir, Kelly Blincoe
TL;DR
Vibe coding is widely practiced, but systematic evidence about its motivations, experiences, and quality practices is limited. This paper conducts a systematic grey-literature review of practitioner accounts and finds that speed and accessibility often accompany fragile code and weak verification. The findings identify implications for responsible tool and team practices.
Problem
No research had systematically examined vibe coding as a distinct practice or users’ motivations, experiences, QA practices, and perceptions of generated-code quality.
Method
The study systematically reviews grey literature and analyzes firsthand behavioral accounts of vibe coding using behavioral search indicators and practitioner language.
Results
Vibe coders are primarily motivated by speed and accessibility, while minimal review, skipped testing, delegated QA, and uncritical trust often leave outputs fragile and difficult to debug.
Takeaways & Limitations
Tool designers and software teams should support review and validation through QA feedback, code-quality indicators, and inline explanations.
Takeaways & Limitations
Because the 101 sources reflect practitioners who chose to share stories online, the findings are indicative patterns rather than generalizable estimates for all vibe coders.
Abstract
from arXiv · showhide
AI code generation tools are transforming software development, especially for novice and non-software developers, by enabling them to write code and build applications faster and with little to no human intervention. Vibe coding is the practice where users rely on AI code generation tools through intuition and trial-and-error without necessarily understanding the underlying code. Despite widespread adoption, no research has systematically investigated why users engage in vibe coding, what they experience while doing so, and how they approach quality assurance (QA) and perceive the quality of the 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 are motivated by speed and accessibility, often experiencing rapid ``instant success and flow'', yet most perceive the resulting code as fast but flawed. QA practices are frequently overlooked, with many skipping testing, relying on the models' or tools' outputs without modification, or delegating checks back to the AI code generation tools. This creates a new class of vulnerable software developers, particularly those who build a product but are unable to debug it when issues arise. We argue that vibe coding lowers barriers and accelerates prototyping, but at the cost of reliability and maintainability. 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.
1 Introduction
Vibe coding emerged as a fast, accessible approach to software creation in which users prompt AI tools with limited code review. The paper addresses the gap in systematic evidence about users’ motivations, experiences, and quality practices.
- Context: AI code generation tools let users describe functionality in natural language and receive executable code, lowering barriers for people with limited programming experience.The tools also enable people without formal training to build functional applications.
- Vibe coding: Vibe coding prioritizes speed and experimentation over understanding by relying on natural-language descriptions and limited understanding of generated code.The practice spread rapidly alongside AI code generation tools.
- Benefits and risks: Vibe coding offers accessibility, speed, and creative potential, but raises concerns about learning, security, technical debt, quality, and maintainability.Prior studies and practitioner commentary report vulnerabilities and adoption of generated code without understanding.
- Research gap: The paper fills a research gap by systematically examining vibe coding as a distinct, intuition-driven trial-and-error practice.It analyzes motivations, hands-on experiences, and quality-assurance practices documented in firsthand grey-literature accounts.
- Contribution: Users are primarily motivated by speed and accessibility, while minimal review often produces fragile or error-prone code.The findings inform tools and teams seeking to encourage review and validation.
2 Related Work
Prior research covers AI-assisted programming across learners, novices, professionals, prompting, security, and perceptions, but does not directly examine vibe coding as a distinct practice. This gap motivates a grey-literature synthesis of firsthand behavioral accounts.
- Learners and novices: Studies of students and beginners report increased confidence or working programs alongside limited understanding of AI-generated code and confusion when errors arise.One comprehension study reported 32.5% success, with unfamiliar styles, automation bias, and limited experience identified as challenges.
- Learners and novices: Non-software developers can generate basic programs but struggle to express intent clearly and verify whether AI-generated code is correct.These findings identify barriers that remain despite expanded access to software creation.
- Professional developers: Junior developers often rely on trial-and-error and accept suggestions without full understanding, whereas experienced developers use AI for familiar code or unfamiliar ideas and generally test results carefully.Professional use therefore differs from the intuition-driven practice targeted by this paper.
- Prompting: Prompt-engineering research examines prompt design but does not specifically address vibe coding, where intuition and trial-and-error prompting dominate.The distinction is between prompt construction and the broader behavioral practice under study.
- Security and perceptions: Security studies report vulnerabilities in AI-generated code, including approximately 40% of 1,689 Copilot programs, with weaknesses also found after safety layers and in GitHub projects.These studies concern AI-generated code risks rather than vibe coding as a distinct practice.
- Research gap: Overall, prior work does not directly investigate vibe coding as an intuition-driven trial-and-error practice distinct from general AI-assisted software development.The review addresses this gap by synthesizing firsthand accounts across diverse user groups.
3 Methodology
The study uses an iterative grey-literature search designed around practitioner language and behavioral indicators of vibe coding. Search terms were tested, refined, validated against known sources, and supplemented with backward snowballing until thematic saturation.
- Grey-literature review: Grey literature provides practitioner perspectives through blogs, technical reports, and web articles that may not appear in peer-reviewed research.The review treats these sources as evidence of real-world software practices.
- Review process: The review followed established guidelines, searched Google, and used backward snowballing to identify relevant grey-literature sources.Figure 1 outlines the review process.
- Search strategy: The search defined vibe coding through intuition, trial and error, limited understanding, and limited review or testing, so it targeted behavioral indicators rather than technical descriptions.This operational focus distinguishes the search from broader AI-assisted programming searches.
- Search refinement: The initial candidate-term combination screened 100 results and found 8% relevant sources, mainly missing firsthand vibe-coding behavior.The stage helped identify terminology that was too broad or insufficiently practitioner-focused.
- Search refinement: Additional semantically adjacent terms produced relatively few relevant hits, with 12% relevance among 100 screened results.Terms were retained only when they increased retrieval relevance.
- Validation and stopping: The final search retrieved all quasi-gold-standard items, continued screening to thematic saturation, and applied backward reference chaining.The quasi-gold set contained known firsthand accounts involving prompting, minimal oversight, trial-and-error acceptance, or skipped QA.
3.3 Inclusion and Exclusion Criteria
The review included publicly accessible English-language sources from 2022–2025 that contained identifiable firsthand behavioral evidence of vibe coding. It excluded duplicated, promotional, purely technical, unsupported, or behaviorally irrelevant material.
- Inclusion criteria: Included sources were English-language, publicly accessible, dated 2022–2025, relevant to the research questions, and attributable to an author or publishing entity.These criteria establish the review’s source and accessibility boundaries.
- Inclusion criteria: Included behavioral evidence covered AI-generated code used as-is or lightly edited, prompting and reprompting, and minimal or absent testing, review, or improvement.Critical reflections were accepted when based on firsthand vibe-coding experience.
- Exclusion criteria: Excluded material included duplicates, irrelevant or purely technical content, promotional sources, undated or anonymous sources, and very short content without commentary.Tool descriptions without user insight were also excluded.
- Exclusion criteria: Sources were excluded when they offered speculation or opinions without observable vibe-coding actions such as prompting, accepting generated code, or skipping QA.The exclusion rule kept the dataset focused on behavioral evidence.
3.4 Quality Assessment
The review assessed grey-literature source quality using five criteria, scoring each source from 0 to 3 per dimension for a maximum of 15.
- Scoring procedure: Each source received a 0–3 score across the five quality dimensions, producing a maximum total score of 15.The assessment followed Garousi et al.’s grey-literature quality framework.
- Quality criteria: Sources were evaluated for Authority, Evidence, Objectivity, Currency, and Purpose.Currency required publication between 2022 and 2025, while Purpose excluded promotional or marketing intent.
3.5 Data Filtration:
The search identified 154 grey-literature sources, of which 101 satisfied the review’s inclusion criteria and quality thresholds.
- Filtering results: 101 of 154 retrieved sources met the inclusion criteria and quality thresholds.The remaining 53 sources were excluded.
- Exclusion reasons: 40 sources were excluded for failing to reach the minimum QA threshold of 10/15.Another 13 were excluded under the review’s inclusion and exclusion criteria.
3.6 Behavioral Unit Extraction
The review converted source material into 518 quote-level behavioral units, separately recording motivations, experiences, code-quality perceptions, and QA practices.
- Extraction scope: 518 behavioral units were extracted from 101 included sources.Each unit represented a distinct coded instance relevant to the research questions.
- Extraction scope: The extraction operated at quote level, allowing multiple distinct behaviors from one article to remain separate.For example, time-saving motivation and skipped QA could be represented as different units.
- Corpus composition: 140 motivation, 132 experience, 114 code-quality perception, and 132 QA-practice units formed the raw data corpus.A subset of extracted units was cross-validated by another co-author.
3.7 Data Analysis
The authors analyzed the extracted behavioral units thematically, organizing patterns around motivations, experiences, code-quality perceptions, and QA practices to construct final vibe-coding themes.
- 3.7 Data Analysis: Thematic analysis followed Braun and Clarke’s procedures and was guided by the review’s research questions.The analysis organized behavioral patterns across motivations, experiences, code quality, and QA practices.
- 3.7 Data Analysis: The researchers repeatedly familiarized themselves with all 518 units before assigning initial codes.Examples included uncritical trust in AI-generated code and speed-related behavior.
- 3.7 Data Analysis: Initial codes such as Uncritical Trust, False Confidence, and Uncritical Security Trust were grouped into the final theme Uncritical Trust.This illustrates the progression from individual codes to broader candidate and final themes.
- 3.7 Data Analysis: The final themes were defined for RQ1 through RQ4, while Figure 2 presents their corresponding frequencies.Table 1 summarizes the theme definitions used for coding.
4 Results
Across 101 sources, the review identified themes covering motivations, experiences, perceived code quality, and QA practices in vibe coding. Speed and accessibility supported rapid, enjoyable development, while practitioners also reported flawed code and frequently skipped QA.
- Results overview: 518 behavioral units were identified: 140 on motivations, 132 on experiences, 114 on perceived code quality, and 132 on QA practices.These units were coded into themes corresponding to the four research questions.
- Motivations: Speed and efficiency was the leading motivation theme at 62%, while accessibility and empowerment accounted for 14%.Practitioners valued rapid development and the ability to build applications without formal training.
- Experiences: Instant success and flow was the most common experience theme at 64%, whereas prompt struggle and iteration accounted for 13%.Some practitioners achieved working applications in minutes, while others required repeated prompt adjustment and code refinement.
- Perceived code quality: Fast but flawed was the dominant perceived code-quality theme at 68%, followed by fragile or error-prone code at 19%.Practitioners described rapid generation alongside production-readiness challenges, technical debt, hidden logic errors, performance bottlenecks, and security flaws.
- QA practices: Skipped QA was the most common QA practice at 36%, compared with manual testing or edits at 29% and delegated QA to AI at 10%.Some users accepted code without tests or structured review, while others relied on AI tools to generate fixes for reported errors.
5 Discussion
The discussion identifies a speed–quality trade-off in vibe coding: rapid prototyping and accessibility coexist with flawed code, overlooked QA, and heightened vulnerability for users unable to debug. It recommends guardrails, skill-matched scaffolds, and further research into expertise-sensitive practices and effective QA workflows.
- Speed–Quality Trade-off Paradox: Vibe coders accept flawed AI-generated code for rapid progress, while experienced developers more often modify or test outputs before use.29% of experienced developers reported usually making manual adjustments or adding tests.
- Recommendation for Practitioners: Vibe-coded applications should not reach production without tests, code review, traceable decision records, and other guardrails.Recommended records include accepted risks, passed checks, and short prompt/response identifiers.
- The QA Crisis in AI-Assisted Development: QA commonly breaks down through skipped tests, uncritical trust in outputs, or delegation of verification back to AI tools.Reported causes include difficult-to-debug code, confusion, and false confidence from instant success.
- Recommendation for AI code generation tools designers: AI code-generation tools should provide lightweight verification, code analysis, and explanations that clarify generated code and potential risks.Suggested interfaces include real-time indicators, walkthroughs, visual diagrams, and inline explanations.
- New Class of Vulnerable Developers: About 14% of vibe coders cite accessibility and empowerment as motivations, but some non-software developers reach dead ends when bugs or technical errors arise.Documented risks include copy-paste fixes and insecure systems lacking authentication, authorization, or containing hardcoded secrets.
- Recommendation for Organizations and Practitioners: Organizations should match tasks to user skill and provide guided debugging, safe templates, and escalation paths to prevent reprompt–paste loops.The proposed scaffolds aim to help newcomers diagnose issues rather than outsource all QA to AI.
- Future Work and Open Research Questions: Future research should examine how practices change with experience and which review strategies work under vibe coding conditions.The findings motivate expertise-adaptive safeguards and empirical study of defect and vulnerability patterns.
6 Threats to Validity
The review addresses validity threats through structured search, quality screening, standardized extraction, and thematic-analysis procedures. Nevertheless, incomplete coverage, residual bias, subjective coding, and limited generalizability constrain interpretation.
- The comprehensive, iteratively refined search reduced search-string bias, but the diffuse and evolving grey-literature landscape means relevant sources may have been missed.
- Sources scoring below 10/15 on a five-dimension quality checklist were excluded, reducing anecdotal or promotional influence while leaving some quality-bias risk.
- Standardized extraction and structured thematic analysis reduced emphasis bias, but coding remained subject to researcher judgment.
- The 101 sources represent practitioners who chose to share experiences online, so findings indicate reported patterns rather than generalizable evidence about all vibe coders.
7 Conclusion
The study provides an empirical account of vibe coding by systematically analyzing firsthand practitioner accounts. It finds that rapid application creation is accompanied by verification and maintainability risks, requiring more deliberate quality practices as AI coding expands.
- 101 grey-literature sources containing 518 firsthand behavioral units provide the first empirical investigation of how users engage in vibe coding.
- The review examines users’ motivations, experiences, perceptions of AI-generated code quality, and practices for reviewing or testing that code.
- Vibe coding creates a speed–quality trade-off: users can build usable applications quickly, often at the expense of verification and maintainability.
- Skipped testing, uncritical trust, and delegated QA leave some developers able to build products but unable to debug them.
- Tool designers are encouraged to add QA feedback, code-quality indicators, and inline explanations, while teams should use review guardrails and retain debugging skills.
- The future impact of AI code-generation tools on software quality depends on how wisely the identified behavioral patterns are addressed.