Related papers: Vibe Coding in Software Development: A Multivocal Literature Review

Vibe Coding in Software Development: A Multivocal Literature Review

URL: http://arxiv.org/abs/2607.21652v1
Date: Wed, 22 Jul 2026 17:43:41 GMT
Title: Vibe Coding in Software Development: A Multivocal Literature Review
Abstract summary: Vibe coding is a software development practice in which developers state intent in natural language and large language models generate code.<n>To our knowledge, existing reviews have not yet integrated peer-reviewed and grey literature on vibe coding.<n>This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol.
Score: 2.1794203097646125
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Vibe coding is a software development practice in which developers state intent in natural language and large language models generate code. It is often framed as one-shot prompting, but the evidence describes an intent-driven, iterative workflow whose outcomes depend on how generated code is evaluated and governed. Knowledge of how vibe coding is defined, practiced, and governed is scattered across academic and practitioner sources, and, to our knowledge, existing reviews have not yet integrated both evidence streams. We conducted a multivocal literature review of peer-reviewed and grey literature following established guidelines. Searches spanned 2022 to October 2025. After screening, credibility assessment, and snowballing, 47 sources were retained (28 peer-reviewed and 19 grey) and analyzed through descriptive mapping and thematic synthesis across eight research questions. Vibe coding is consistently described as an iterative generation-evaluation-revision loop rather than a one-shot activity, and developer work shifts from writing code towards specification, supervision, and validation. Short-term productivity and time-to-prototype gains are reported in 21 of 47 sources (45%), while evidence on maintainability, long-term quality, and safeguard effectiveness remains limited. Evidence is strongest for prototyping and user-interface work and weakest for production, data-intensive, and safety-critical use, and tool visibility does not imply effectiveness. This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol. Future work should evaluate safeguard effectiveness, study session-level dynamics and long-term maintainability, and test vibe coding in production, data-intensive, and safety-critical settings.

Related papers

Vibe Coding: Practice, Performance, Productivity, and Risk -A State-of-the-Art Review [5.061021429555365]
State-of-the-art review assembles that evidence across a cross-disciplinary corpus spanning software engineering, human-computer interaction, labour economics, security research, governance, and education.<n>We survey the model landscape, the tool ecosystem, and the performance record by task type, finding the early benchmarks saturated but task-level capability uneven.<n>We document security failures in deployed applications, code-quality degradation visible in large-scale code and developer telemetry, unsettled copyright exposure, and evidence of skill atrophy.
arXiv Detail & Related papers (2026-08-20T16:10:28Z)
AI for Auto-Research: Roadmap & User Guide [107.0834449839233]
We present an end-to-end analysis of AI across the complete research lifecycle.<n>We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy.<n>We show that greater automation can obscure rather than eliminate failure modes.
arXiv Detail & Related papers (2026-05-18T17:08:26Z)
Prompt-Driven Code Summarization: A Systematic Literature Review [4.238464304669881]
Large language models (LLMs) offer a promising solution by automatically generating natural language descriptions from source code.<n>This systematic review literature consolidates existing evidence, categorizes prompting paradigms, examines their effectiveness, and identifies gaps to guide future research and practical adoption.
arXiv Detail & Related papers (2026-04-16T03:25:28Z)
Readability-Robust Code Summarization via Meta Curriculum Learning [53.44612630063336]
In the real world, code is often poorly structured or obfuscated, significantly degrading model performance.<n>We propose RoFTCodeSum, a novel fine-tuning method that enhances the robustness of code summarization against poorly readable code.
arXiv Detail & Related papers (2026-01-09T02:38:24Z)
Building Software by Rolling the Dice: A Qualitative Study of Vibe Coding [15.145249560710377]
"vibe coders" build software primarily through prompts rather than writing code.<n>We conducted a theory study of 20 vibe-coding videos, including 7 live-streamed coding sessions and 13 opinion videos.<n>Our findings reveal a spectrum of behaviors: some vibe coders rely almost entirely on AI, while others examine and adapt generated outputs.
arXiv Detail & Related papers (2025-12-27T00:38:37Z)
CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
arXiv Detail & Related papers (2025-08-28T06:03:11Z)
Leveraging Reward Models for Guiding Code Review Comment Generation [13.306560805316103]
Code review is a crucial component of modern software development, involving the evaluation of code quality, providing feedback on potential issues, and refining the code to address identified problems.<n>Deep learning techniques are able to tackle the generative aspect of code review, by commenting on a given code as a human reviewer would do.<n>In this paper, we introduce CoRAL, a deep learning framework automating review comment generation by exploiting reinforcement learning with a reward mechanism.
arXiv Detail & Related papers (2025-06-04T21:31:38Z)
Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
arXiv Detail & Related papers (2025-05-28T17:57:47Z)
Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z)
Investigating the Impact of Vocabulary Difficulty and Code Naturalness on Program Comprehension [3.35803394416914]
This study aims to assess readability and understandability from the perspective of language acquisition. We will conduct a statistical analysis to understand their correlations and analyze whether code naturalness and vocabulary difficulty can be used to improve the performance of readability and understandability prediction methods.
arXiv Detail & Related papers (2023-08-25T15:15:00Z)
A Transformer-based Approach for Source Code Summarization [86.08359401867577]
We learn code representation for summarization by modeling the pairwise relationship between code tokens. We show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
arXiv Detail & Related papers (2020-05-01T23:29:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.