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Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review (2510.00328v1)
Published 30 Sep 2025 in cs.SE
Abstract: 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.
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Easy Explanation of “Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook — a Grey Literature Review”
What this paper is about
This paper looks at a new way people write software called “vibe coding.” In vibe coding, someone tells an AI tool (like ChatGPT or GitHub Copilot) what they want in plain English and lets the AI write most or all of the code. People often do this quickly and don’t always understand or carefully check the code the AI produces.
The authors wanted to understand why people vibe code, what it feels like to do it, how good the AI-written code is, and how (or if) people check that code for mistakes.
What the researchers wanted to find out
The paper focuses on four simple questions:
- Why do people choose to vibe code?
- What is it like to vibe code (what goes well and what goes wrong)?
- How do people think about the quality of the AI-generated code?
- What kind of testing or quality checks do people actually do when vibe coding?
How they did the paper (in simple terms)
Instead of studying only formal scientific papers, the authors looked at “grey literature.” That means everyday sources people actually read and write online: blog posts, forum threads, tech articles, personal write-ups, and similar posts. Think of it like gathering real stories and experiences from the internet.
Here’s their easy-to-understand process:
- They searched the web (mostly using Google) for posts where people talked about using AI tools to code through trial-and-error and minimal checking.
- They set clear rules to include only useful, trustworthy sources (for example, posts had to have a real author, be recent, and describe real behavior).
- They rated each source’s quality (like “Is the author credible?” “Is it current?” “Does it show evidence?”).
- From the chosen sources, they pulled out 518 “behavioral units.” You can think of a “behavioral unit” as one specific example or quote about a person’s motivation, experience, opinion on quality, or testing habits.
- They used “thematic analysis,” which is a fancy way of saying they grouped similar ideas to find patterns. For example, many posts saying “It was super fast!” would be grouped into a “speed” theme.
Analogy: Imagine reading hundreds of movie reviews and tagging each sentence with labels like “funny,” “boring,” “great acting,” then counting which tags show up most. That’s basically what they did, but with coding stories.
What they found (and why it matters)
Overall, the paper found a big speed–quality trade-off: vibe coding is fast and exciting, but the code often isn’t very reliable.
Here are the main patterns:
- Why people vibe code
- Speed was the #1 reason (reported most often). People could build something in hours instead of weeks.
- Accessibility was next: non-programmers could make working apps just by describing what they wanted.
- Learning and experimentation were also common: people used AI as a tutor or playground to try new tools and languages.
- What vibe coding feels like
- Many people felt “instant success and flow.” It can feel magical when your idea quickly turns into a working app.
- But lots of users struggled with prompts, needing many tries to get the AI to do exactly what they wanted.
- Some projects fell apart when the code became too buggy or too complex to fix, leading people to give up.
- How people see the quality of AI-written code
- The most common view was “fast but flawed.” The code works quickly for a demo, but it may be messy, fragile, or risky for real, long-term use.
- Many described the code as fragile or error-prone, with hidden bugs or security issues that might appear later.
- What people do for testing or quality checks (QA)
- The most frequent behavior was skipping QA. Many didn’t write tests or do careful reviews; they just ran the code and, if it didn’t crash, they kept it.
- Some did careful manual checks and edits—but they were a minority.
- A noticeable group trusted the AI too much, or even asked the AI to check or fix its own mistakes, instead of learning to debug themselves.
Why this matters: If many people—especially beginners—ship AI-written code without proper checks, we could end up with lots of software that works at first but breaks easily, is hard to fix, or has security holes.
What this could mean for the future
- For tool designers: Build AI coding tools that nudge users to review, test, and understand the code, not just accept it. For example, include built-in tests, warnings about risky patterns, or explainers that teach what the code does.
- For teachers and learners: Teach how to test and verify AI-generated code. Using AI is helpful, but understanding and checking the result is essential.
- For software teams: Set clear rules for using AI-generated code (code reviews, tests, security checks) so speed doesn’t compromise safety and reliability.
Simple takeaway: Vibe coding makes it easy and fast to turn ideas into working software. That’s exciting and empowering. But without careful checking, the code can be unreliable. The next step is making AI-assisted coding both fast and trustworthy—so people can build cool things quickly and safely.
Knowledge Gaps
Unresolved knowledge gaps, limitations, and open questions
The paper surfaces important themes about vibe coding but leaves several concrete gaps that future research should address:
- Quantify the speed–quality trade-off with controlled studies across task types, measuring time-to-completion, defect density, maintainability, performance, and security outcomes.
- Operationalize a measurable definition of “vibe coding” (e.g., thresholds for code review depth, prompt iteration, test coverage) and validate it with inter-rater reliability in diverse contexts.
- Assess representativeness of the grey literature corpus: systematically capture demographics, user roles, domains, tools, languages, and project types to evaluate selection bias.
- Improve search replicability: account for Google ranking variability, compare alternative discovery channels (e.g., GitHub, Stack Overflow, Reddit), and archive queries/results for repeatability.
- Resolve reporting incompleteness: replace OOO placeholders with exact counts (included sources, excluded sources, units), and provide a PRISMA-style flow diagram.
- Report coder agreement statistics (e.g., Cohen’s kappa) for both thematic coding and quality assessment beyond a “subset cross-validated” claim.
- Compare outcomes across user segments (novices, juniors, professionals, non-developers) and settings (personal prototypes vs production systems) to identify boundary conditions for safe use.
- Conduct longitudinal studies tracking maintainability, technical debt, and sustainability of vibe-coded projects over months/years.
- Quantify real-world security risk in vibe-coded repositories: prevalence of vulnerabilities, exploitability, time-to-fix, and downstream incident rates via static analysis and SCA.
- Test the effectiveness of “delegated QA to AI” (self-checking, test generation, static analysis suggestions) versus human QA and hybrid workflows in randomized or quasi-experimental designs.
- Evaluate tool features that may reduce uncritical acceptance (e.g., mandatory validation gates, provenance/explanations, test coverage prompts, risk badges, linting-on-suggest) through usability and outcome studies.
- Investigate trade-offs between prompt engineering training and testing/debugging training; identify combinations that maximize quality without negating speed gains.
- Analyze domain and language effects: outcomes across strongly typed vs dynamic languages, multi-file/architecture-heavy projects, and safety-critical domains (e.g., finance, healthcare).
- Identify complexity thresholds where vibe coding tends to fail; develop diagnostics or guardrails to warn users and recommend switching workflows or adding QA steps.
- Develop reliable detectors of vibe coding in the wild (commit metadata, style signals, AI-generated code classifiers) to estimate prevalence and monitor trends in repositories.
- Measure team-level impacts: changes to code review burden, CI/CD quality gates, on-call reliability, incident rates, and knowledge transfer when vibe coding is adopted in collaborative environments.
- Design and evaluate educational interventions (checklists, scaffolds, curricula) that instill QA habits in vibe coders while preserving rapid prototyping benefits.
- Study cognitive and trust dynamics (automation bias, illusions of understanding); test UI cues and explanations that calibrate user confidence in generated code.
- Map legal and ethical risks (IP licensing, attribution, data privacy, accountability for defects) specific to vibe-coded software and evaluate mitigation practices.
- Examine handoff processes: when non-developers vibe-code prototypes, what practices enable smooth transition to engineering teams without costly rework?
- Perform comparative analyses across tools and versions (e.g., Copilot vs ChatGPT vs others) and integrations (IDE vs chat, voice), isolating variability drivers in outcomes.
- Create a taxonomy of code-specific hallucinations (error types, triggers, severity) and evaluate detection/mitigation strategies.
- Build cost–benefit models that incorporate speed gains, QA effort, defect costs, and rework to guide organizational policies on vibe coding use.
- Expand cultural and linguistic coverage beyond English and extend the time window to capture diverse practices and model/tool evolution effects.
- Address grey literature fragility (link rot, content changes) by establishing archiving/versioning protocols and periodic re-reviews to maintain evidence integrity.
Practical Applications
Overview
Drawing on the paper’s grey literature synthesis of “vibe coding” (intuition-driven, trial-and-error use of AI code generation with minimal review), the following applications translate its findings into concrete tools, workflows, policies, and practices. Applications are grouped by time horizon and linked to sectors where relevant. Each item notes key assumptions or dependencies that affect feasibility.
Immediate Applications
These can be piloted or deployed now using current LLMs, IDEs, CI/CD, and governance practices.
- AI-origin provenance and risk-gating in software delivery [Software, Security/Compliance, Finance, Healthcare]
- Tag AI-generated diffs at the IDE or VCS hook, propagate tags through CI, and enforce stricter review/testing gates for AI-origin code (e.g., mandatory tests, second reviewer, SAST/DAST, secrets scan).
- Tools/products: IDE plugins for provenance tagging, Git hooks, CI policies (GitHub Actions/GitLab CI), “LLM-aware” SAST rulesets.
- Assumptions: Access to IDE/VCS integration; buy-in to modify merge policies; low false positives in AI-origin detection.
- “Prototype sandbox” environments separated from production workflows [Software, Product, Regulated sectors]
- Create dedicated vibe-to-prototype lanes with permissive iteration but hard boundaries (no prod access, ephemeral data, auto-teardown), explicit “prototype-only” labels, and conversion checklists before graduation to production.
- Tools/products: Ephemeral environments (e.g., preview deployments), feature flags, environment-level policy as code.
- Assumptions: Clear environment separation; product discipline to honor “prototype” labels; governance alignment.
- QA-first AI assistant prompts and templates [Software, Education]
- Provide prompt templates that demand tests, docstrings, edge cases, and threat models by default; add “prompt-to-test” scaffolding so every generation is paired with unit tests and runtime assertions.
- Tools/products: Prompt libraries, IDE snippets, test harness generators, “generate-then-test” commands.
- Assumptions: Users will adopt scaffolds; models can produce minimally useful tests; team norms reward tests.
- “Delegate QA to tools, not vibes” bundles [Security, DevOps]
- Package SAST/IAST/secret scanning + dependency vulnerability checks tuned to common LLM mistakes (hardcoded creds, insecure defaults, injection/sanitization gaps).
- Tools/products: Curated rule packs for common LLM error patterns; pre-commit hooks; CI bundles.
- Assumptions: Scanner coverage and precision; maintenance of rule packs as models evolve.
- Trust calibration training for developers and “citizen coders” [Education, HR/L&D, SMBs]
- Short courses that teach: when vibe coding is appropriate, how to review AI code, how to reprompt vs. debug, and how to validate via runtime checks and tests; include checklists and “red flag” heuristics.
- Tools/products: Microlearning modules, IDE-instructor overlays that nudge reviews, lab exercises with seeded LLM bugs.
- Assumptions: Training time; safe datasets; willingness to change habits.
- Organizational policy for AI-generated code usage [Policy/Governance, Enterprise IT]
- Define permissible use (prototyping vs production), required documentation (prompt logs, model/version), QA requirements by risk tier, and disclosure in change records/SBOMs.
- Tools/products: Policy templates, SBOM extensions for AI provenance, prompt-log retention in version control.
- Assumptions: Legal review; data retention rules; integration with existing change-management.
- Vibe coding playbooks for high-risk sectors [Healthcare, Finance, Energy]
- Restrict vibe coding to simulation/synthetic data; require domain-expert review and security sign-off before any real-world use; mandate traceability from prompt to deployment artifact.
- Tools/products: Domain checklists, simulation sandboxes, approval workflows.
- Assumptions: Availability of realistic simulators/synthetic data; domain reviewer capacity.
- Classroom and bootcamp integration: “AI code QA lab” [Academia]
- Assignments where students must critique, test, and harden AI-generated code; grade on QA artifacts (tests, static findings resolved, documentation) rather than only functionality.
- Tools/products: Rubrics emphasizing QA, benchmark tasks with typical LLM errors, plagiarism-safe prompt-logging.
- Assumptions: Instructor readiness; institutional policy alignment on AI use.
- Grey literature monitoring for product risk sensing [Product, Research/UX]
- Establish a lightweight GL watch (blogs, forums) to detect emerging failure modes and user behaviors; feed insights into roadmap and guardrail design.
- Tools/products: Curated feeds, internal briefings, tagging taxonomies aligned to the paper’s themes.
- Assumptions: Analyst bandwidth; consistent taxonomy application.
- SMB/individual maker guardrails [Daily life, SMB automation]
- Practical checklists: use containers/sandboxes, never paste secrets, prefer low-risk automations, run linters/tests, keep backups and version control, document prompts (“prompt diary”).
- Tools/products: One-click “safe project template” with prewired tests and linting; desktop sandboxes (Docker/Dev Containers).
- Assumptions: Minimal dev literacy; simple setup guides.
Long-Term Applications
These require further research, tooling maturity, standardization, or cultural/legal adoption.
- QA-enforcing AI coding assistants (“spec→tests→code→self-check”) [Software, Tooling]
- Assistants that elicit requirements, generate tests first, produce code, then attempt self-repair under constraints; refuse completion if QA thresholds unmet.
- Tools/products: Multi-agent systems, proof obligations, policy-driven refusal modes.
- Dependencies: Robust test generation; reliable self-critique; UX acceptance of enforced gates.
- LLM-aware CI/CD and observability [DevOps, SRE]
- End-to-end pipelines that adapt gates based on AI-origin risk, continuously monitor defect/incident rates attributable to AI code, and trigger refactoring or rollback policies.
- Tools/products: “AI-origin risk score” in CI, telemetry linking incidents to provenance, auto-created debt tickets.
- Dependencies: Provenance fidelity; causality heuristics; developer trust in automated governance.
- Formal methods and verification integrated with AI generation [Safety-critical sectors, Robotics, Energy]
- Model-driven generation constrained by specifications and verified with theorem provers or model checkers; simulation-in-the-loop for robotics/control code.
- Tools/products: Spec languages friendly to LLM prompting, auto-generated invariants, digital twins.
- Dependencies: Usable spec tooling; performance of verifiers; domain simulators.
- Sector-grade regulatory pipelines and certifications [Healthcare, Finance, Public Sector]
- Standardized processes for AI-generated code: provenance in SBOMs, prompt audit trails, safety cases, external audits, and compliance certifications (“AI-Generated Code Safe for Use”).
- Tools/products: SBOM extensions for AI provenance (AI-PBOM), certification schemes, audit APIs.
- Dependencies: Standards bodies consensus; regulator guidance; auditor ecosystem.
- Liability, insurance, and procurement frameworks for AI-generated code [Policy, Legal, Enterprise Procurement]
- Contracts that allocate responsibility for AI-derived defects; insurance products pricing AI-code risk; vendor disclosure obligations and right-to-audit for AI use in supply chains.
- Tools/products: Model contract clauses, actuarial models for AI-code incidents, vendor assessment checklists.
- Dependencies: Legal precedents; incident datasets; market appetite.
- Education transformation: AI-and-QA-centric curricula [Academia]
- Degrees and certificates emphasizing AI-assisted engineering, prompt design, trust calibration, and QA of generated artifacts; capstones on migrating prototypes to production-grade systems.
- Tools/products: Shared open datasets of AI-code failures, standardized benchmarks for “vibe→production” hardening.
- Dependencies: Accreditation updates; faculty development; open educational resources.
- “Explainable code generation” and rationale alignment [Software Engineering]
- Generators that produce traceable design decisions, dependency rationales, and security justifications alongside code; IDEs that reconcile rationale with code diffs over time.
- Tools/products: Rationale artifact formats, diff-aware explanation trackers.
- Dependencies: Model capability to produce faithful rationale; evaluation methods for faithfulness.
- Cross-model self-checking and consensus pipelines [Tooling, Security]
- Use diverse models/tools (LLMs, static analyzers, fuzzers) to cross-validate outputs; escalate on disagreement.
- Tools/products: Orchestration frameworks, disagreement detectors, policy-resolution strategies.
- Dependencies: Cost/performance of ensemble checks; interoperability.
- Socio-technical monitoring of trust and skill erosion [HR, Org Design]
- Longitudinal metrics of skill retention, debugging competence, and trust in automation to avoid “new class of vulnerable developers.”
- Tools/products: Skill telemetry in IDEs, periodic competency assessments, targeted upskilling plans.
- Dependencies: Privacy-compliant telemetry; cultural acceptance.
- Consumer-grade “guardian” layers for end users [Daily life, SMBs]
- OS/IDE-level protections that flag high-risk patterns (e.g., unsafe evals, insecure network calls) in pasted AI code; interactive wizards to add tests and sandboxing automatically.
- Tools/products: Browser/IDE extensions with policy packs; one-click containerization.
- Dependencies: Platform cooperation; low-friction UX; accurate detection rules.
Notes on Assumptions and Dependencies Across Applications
- Model capability and cost: Higher guardrails may increase latency and expense; ensembles amplify this.
- Integration friction: Success depends on seamless IDE/CI integration and low false positives in scanners and provenance tagging.
- Culture and incentives: Teams must value QA and safety; grading rubrics, review policies, and leadership messaging are pivotal.
- Legal and regulatory clarity: Especially for healthcare/finance/public sector; standards for AI provenance in SBOMs and audit requirements are still evolving.
- Data governance: Prompt logs and provenance retention must respect privacy, IP, and security constraints.
- Education pipeline: Instructor preparedness and institutional policies influence how quickly curricula can adapt.
These applications directly operationalize the paper’s core insights: leverage vibe coding for speed and accessibility in low-risk contexts, while systematically countering the documented QA gaps (skipped testing, uncritical trust, delegating checks to the same model) with tooling, process, governance, and education.
Glossary
- AI Hallucinations: Incorrect or fabricated outputs produced by AI that appear plausible. "AI Hallucinations: Facing false, inaccurate, or misleading code suggestions that look plausible but fail in execution or introduce bugs."
- Automation bias: The tendency to overtrust automated systems and accept their outputs without sufficient scrutiny. "Zi et al.\cite{zi2025would} similarly found that CS1 students struggled to understand LLM-generated code, with only 32.5% success in comprehension tasks due to unfamiliar coding styles, automation bias, and limited experience."
- Backward snowballing: A literature search method that locates additional sources by following references from included items. "We also applied backward snowballing (following links and references inside included sources) to find further relevant sources (see Section~\ref{FinalSearchFiltration})."
- Behavioral unit: A single coded data point capturing a specific behavior relevant to the research questions. "A behavioral unit is a single coded instance that captures something relevant to the RQs."
- Delegated QA to AI: Relying on AI tools to check and correct their own outputs instead of performing independent validation. "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."
- Grey literature (GL): Non–peer-reviewed sources such as blogs, web articles, and technical reports. "Grey literature encompasses sources that are not published in traditional peer-reviewed venues, including blogs, technical reports, and web articles."
- Grey literature review (GLR): A systematic analysis of grey literature sources to extract evidence. "we conduct a systematic grey literature review (GLR) to analyze firsthand behavioral accounts of vibe coding documented in blogs, forums, media articles, and other publicly available sources."
- LLMs: AI models trained on vast text corpora to understand and generate natural language. "Recent progress in LLMs, accessible through AI code generation tools, such as GitHub Copilot and ChatGPT, is rapidly transforming software development."
- Minimum viable product (MVP): The simplest functional version of a product used to test ideas quickly. "Fast Prototyping & Quickly assembling MVPs or demos to test feasibility, concepts, or market interest."
- Proof of concept (PoC): A prototype demonstrating feasibility without production-level robustness. "Prototype-Ready Only & Adequate for demos and proofs-of-concept but not for long-term systems."
- Prompt engineering: The practice of crafting and refining prompts to improve AI-generated outputs. "In prompt engineering, Kruse et al. \cite{kruse2024can} investigated how developers craft prompts for code generation and how their experience influenced outcomes."
- Quality assurance (QA): Processes and activities aimed at ensuring software correctness, reliability, and quality. "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."
- Quasi-Gold Standard: A validation approach that uses a curated set of known-relevant sources to assess search effectiveness. "we employed a quasi-gold standard evaluation approach."
- Run-and-See Validation: A minimal check where code is executed to see if it runs, equating execution success with correctness. "Run-and-See Validation & Running code to check if it works, equating success with correctness."
- Technical debt: The implied cost of future rework caused by choosing expedient solutions over robust ones. "Practitioners have also highlighted risks of technical debt \cite{GL005, edwards2025vibecoding}."
- Thematic analysis: A qualitative method for identifying and organizing patterns (themes) in data. "We used thematic analysis to analyze the extracted behavioral units of grey literature data, following the procedures recommended by Braun {paper_content} Clarke \cite{braun2006using}."
- Thematic saturation: The point in data collection when additional sources yield no substantially new themes. "then continued until we reached thematic saturation (additional screening no longer surfaced substantially new related sources)."
- Vibe coding: An intuition-driven programming practice that relies on AI-generated code with minimal understanding or review. "we define vibe coding as the practice of using AI code generation tools to produce software primarily by describing goals in natural language and iteratively prompting, while relying on minimal review of the generated code."
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