mlsanityData to 14 Oct 2026

Feedback-based Automated Verification in Vibe Coding of CAS Adaptation Built on Constraint Logic

arXiv 2602.18607 ↗First posted 20 Feb 2026cs.AIcited by 1

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Abstract (arXiv)

In CAS adaptation, a challenge is to define the dynamic architecture of the system and changes in its behavior. Implementation-wise, this is projected into an adaptation mechanism, typically realized as an Adaptation Manager (AM). With the advances of generative LLMs, generating AM code based on system specification and desired AM behavior (partially in natural language) is a tempting opportunity. The recent introduction of vibe coding suggests a way to target the problem of the correctness of generated code by iterative testing and vibe coding feedback loops instead of direct code inspection. In this paper, we show that generating an AM via vibe coding feedback loops is a viable option when the verification of the generated AM is based on a very precise formulation of the functional requirements. We specify these as constraints in a novel temporal logic FCL that allows us to express the behavior of traces with much finer granularity than classical LTL enables. Furthermore, we show that by combining the adaptation and vibe coding feedback loops where the FCL constraints are evaluated for the current system state, we achieved good results in the experiments with generating AMs for two example systems from the CAS domain. Typically, just a few feedback loop iterations were necessary, each feeding the LLM with reports describing detailed violations of the constraints. This AM testing was combined with high run path coverage achieved by different initial settings.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 20 Feb 2026.

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9 of 27 references.

References · 9

  1. 2024AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testingcited by 35
  2. 2025A Survey of Vibe Coding with Large Language Modelscited by 27
  3. 2025RvLLM: LLM Runtime Verification with Domain Knowledgecited by 17
  4. 2024LLM Security Guard for Codecited by 15
  5. 2025Towards Formal Verification of LLM-Generated Code from Natural Language Promptscited by 8
  6. 2024Exploring the Potential of Large Language Models in Self-adaptive Systemscited by 7
  7. 2024Reimagining Self-Adaptation in the Age of Large Language Modelscited by 4
  8. 2025Leveraging LLMs for Dynamic IoT Systems Generation through Mixed-Initiative Interactioncited by 2
  9. 2019Overview of Logical Foundations of Cyber-Physical Systemscited by 1

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