VibeContract: The Missing Quality Assurance Piece in Vibe Coding
- URL: http://arxiv.org/abs/2603.15691v1
- Date: Mon, 16 Mar 2026 03:30:46 GMT
- Title: VibeContract: The Missing Quality Assurance Piece in Vibe Coding
- Abstract summary: We propose the VibeContract paradigm as a missing piece in vibe coding.<n>We show how it can substantially improve the correctness, robustness, and maintainability of AI-generated code.
- Score: 8.781985324057043
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in large language models (LLMs) have given rise to vibe coding, a style of software development where developers rely on AI coding assistants to generate, modify, and refactor code using natural language instructions. While this paradigm accelerates software development and lowers barriers to entry, it introduces new challenges for quality assurance (QA). AI-generated code can appear correct but often contains hidden logical errors and inconsistencies, creating an urgent need for novel QA approaches. In this vision paper, we propose the VibeContract paradigm as a missing piece in vibe coding. In this approach, high-level natural-language intent is decomposed into explicit task sequences, and task-level contracts are generated to capture expected inputs, outputs, constraints, and behavioral properties. Developers validate these contracts, and traceability is maintained between tasks, contracts, and generated code. Contracts then guide LLMs for testing, runtime verification, and debugging, enabling QA to occur continuously and proactively alongside code generation. We demonstrate how the VibeContract paradigm can substantially improve the correctness, robustness, and maintainability of LLM-generated code through an example project. We argue that VibeContract introduces a structured and verifiable development workflow that transforms vibe coding from a fast but error-prone practice into a predictable, auditable, and trustworthy software engineering process. Finally, we outline the key challenges, design principles, and a forward-looking research agenda required to realize this vision.
Related papers
- On Automated and Explainable Provenance of AI-Generated Code [56.818202904805446]
We argue that the next generation of CodeGenAI tools must be built on a foundation of explainable provenance.<n>We describe a research program combining large-scale empirical studies with post-hoc causal and interpretability techniques.
arXiv Detail & Related papers (2026-08-03T14:50:31Z) - 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) - Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook -- a Grey Literature Review [2.5195922470930614]
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.<n>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.<n>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.
arXiv Detail & Related papers (2025-09-30T22:35:00Z) - ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings [8.0322025529523]
This paper introduces Augmented Code Engineering (ACE), a tool that automates code improvements using validated output.<n>Early feedback from users suggests that AI-enabled helps mitigate code-level technical debt that otherwise rarely gets acted upon.
arXiv Detail & Related papers (2025-07-04T12:39:27Z) - 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) - CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation [20.013757490442064]
We introduce CodeIF, the first benchmark designed to assess the abilities of Large Language Models (LLMs) to adhere to task-oriented instructions.<n>CodeIF encompasses a broad range of tasks, including function synthesis, algorithmic instructions, and code explanation.<n>We conduct extensive experiments with LLMs, analyzing their strengths and limitations in meeting the demands of these tasks.
arXiv Detail & Related papers (2025-02-26T14:19:49Z) - Bridging LLM-Generated Code and Requirements: Reverse Generation technique and SBC Metric for Developer Insights [0.0]
This paper introduces a novel scoring mechanism called the SBC score.<n>It is based on a reverse generation technique that leverages the natural language generation capabilities of Large Language Models.<n>Unlike direct code analysis, our approach reconstructs system requirements from AI-generated code and compares them with the original specifications.
arXiv Detail & Related papers (2025-02-11T01:12:11Z) - 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) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code.
We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
arXiv Detail & Related papers (2023-02-14T18:43:34Z)
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.