Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification

“Vibe coding” — the practice of developing software through iteratively conversing with a large language model (LLM) — has exploded in popularity within the last year. However, developers report key limitations including the accumulation of technical debt, security issues, and code churn to achieve satisfactory results. We argue that these pitfalls result from LLMs’ inability to reconcile accumulating human-imposed constraints during vibe coding, with developers inadvertently failing to resolve contradictions because LLMs prioritize user commands over code consistency. Given LLMs’ receptiveness to verification-based feedback, we argue that formal methods can mitigate these pitfalls, making vibe coding more reliable. However, we posit that integrating formal methods must transcend existing approaches that combine formal methods and LLMs. We advocate for a side-car system throughout the vibe coding process which: (1) Autoformalizes specifications (2) Validates against targets, (3) Delivers actionable feedback to the LLM, and (4) Allows intuitive developer influence on specifications.

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Publication Details

Published
2025-10-09
DOI
https://doi.org/10.1145/3759425.3763390
Citations
1
Primary Topic
Model-Driven Software Engineering Techniques
Type
article
Field-Weighted Citation Impact
2.31
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article

Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification

Jacqueline Mitchell, Yasser Shaaban
1 citations
Model-Driven Software Engineering Techniques
2.31
article

Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification

Jacqueline Mitchell, Yasser Shaaban
article en
1 citations

Abstract

“Vibe coding” — the practice of developing software through iteratively conversing with a large language model (LLM) — has exploded in popularity within the last year. However, developers report key limitations including the accumulation of technical debt, security issues, and code churn to achieve satisfactory results. We argue that these pitfalls result from LLMs’ inability to reconcile accumulating human-imposed constraints during vibe coding, with developers inadvertently failing to resolve contradictions because LLMs prioritize user commands over code consistency. Given LLMs’ receptiveness to verification-based feedback, we argue that formal methods can mitigate these pitfalls, making vibe coding more reliable. However, we posit that integrating formal methods must transcend existing approaches that combine formal methods and LLMs. We advocate for a side-car system throughout the vibe coding process which: (1) Autoformalizes specifications (2) Validates against targets, (3) Delivers actionable feedback to the LLM, and (4) Allows intuitive developer influence on specifications.

University of Southern California (US), Palo Alto Research Center (US)
Openalex Percentile: Top 10%
Model-Driven Software Engineering Techniques
2.31
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Position: Vibe Coding Needs Vibe Reasoning: Improving Vibe Coding with Formal Verification — Jacqueline Mitchell, Yasser Shaaban · (2025) | TGRS Research Map | TGRS