VibeContract: The Missing Quality Assurance Piece in Vibe Coding
Song Wang

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…
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Taxonomy
TopicsSoftware Engineering Research · Software Engineering Techniques and Practices · Advanced Software Engineering Methodologies
