VibeContract: The Missing Quality Assurance Piece in Vibe Coding

TL;DR

VibeContract introduces a structured QA framework for vibe coding with LLMs, decomposing high-level intents into task contracts to improve correctness, robustness, and maintainability of AI-generated code.

Contribution

It proposes the VibeContract paradigm, integrating task-level contracts into vibe coding to enable continuous validation, testing, and debugging of AI-generated software.

Findings

Contracts improve code correctness and robustness

Traceability enhances maintainability and auditability

Framework supports proactive quality assurance

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.