mlsanityData to 7 Oct 2026

Fidelity Probes for Specification--Code Alignment

arXiv 2605.17246 ↗First posted 17 May 2026cs.LG cs.AIcited by 1

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Abstract (arXiv)

Software modernization often relies on natural-language specifications recovered from an existing implementation. Incorrect or missing requirements can lead to defects in the modernized system. We introduce fidelity probes to check a specification against the code and guide its revision. Each probe pairs a question about program behaviour with a reference answer derived from the code. A language model answers the question using only the specification, and the fraction of agreeing answers defines fidelity. Disagreements flag conflicting claims or missing information and guide proposed corrections and additions to the requirements. An LLM generates probes directly from code or phrases facts selected through control-flow, data-flow, and call-graph analysis. We use an observability rule to focus probe questions on behaviour that the modernized system is intended to preserve, including user-visible outputs, changes to stored business data, and interactions with other systems. We revise the specification using fresh probes and evaluate each version on a fixed held-out set. A repair-regression model describes how new failures can offset the gains from repairs. On CardDemo, held-out fidelity improves from 0.59 to 0.89, exceeding free-form revision from source code. We also evaluate the method on industrial systems and public documentation, and assess probe quality and reported defects through human reviews and comparison with an independent requirements audit.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 17 May 2026.

Builds on

14 of 44 references.

References · 14

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  5. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  6. 2023Large Language Models are not Fair Evaluatorscited by 1,050
  7. 2023Teaching Large Language Models to Self-Debugcited by 963
  8. 2020Asking and Answering Questions to Evaluate the Factual Consistency of Summariescited by 376
  9. 2021QuestEval: Summarization Asks for Fact-based Evaluationcited by 241
  10. 2023Clover: Closed-Loop Verifiable Code Generationcited by 62
  11. 2024Enchanting Program Specification Synthesis by Large Language Models using Static Analysis and Program Verificationcited by 55
  12. 2023Lemur: Integrating Large Language Models in Automated Program Verificationcited by 49
2 more
  1. 2026WybeCoder: Verified Imperative Code Generationcited by 4
  2. 2020Hints and Principles for Computer System Designcited by 1

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Citing papers per year: 2026: 1.

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  1. 2025Disproving Program Equivalence with LLMs
  2. 2026Inferring Code Correctness from Specification
  3. 2026Intent-aligned Formal Specification Synthesis via Traceable Refinement
  4. 2025VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Code
  5. 2026Correct Tests Are Not Enough: Measuring and Training Oracle Conversion in Specification-Based Test Generation ↗
  6. 2026When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents
  7. 2026Consistency Meets Verification: Enhancing Test Generation Quality in Large Language Models Without Ground-Truth Solutions
  8. 2025FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation