mlsanityData to 14 Oct 2026

METAMON: Finding Inconsistencies between Program Documentation and Behavior using Metamorphic LLM Queries

arXiv 2502.02794 ↗First posted 5 Feb 2025cs.SEcited by 3

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Abstract (arXiv)

Code documentation can, if written precisely, help developers better understand the code they accompany. However, unlike code, code documentation cannot be automatically verified via execution, potentially leading to inconsistencies between documentation and the actual behavior. While such inconsistencies can be harmful for the developer's understanding of the code, checking and finding them remains a costly task due to the involvement of human engineers. This paper proposes METAMON, which uses an existing search-based test generation technique to capture the current program behavior in the form of test cases, and subsequently uses LLM-based code reasoning to identify the generated regression test oracles that are not consistent with the program specifications in the documentation. METAMON is supported in this task by metamorphic testing and self-consistency. An empirical evaluation against 9,482 pairs of code documentation and code snippets, generated using five open-source projects from Defects4J v2.0.1, shows that METAMON can classify the code-and-documentation inconsistencies with a precision of 0.72 and a recall of 0.48.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Feb 2025.

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Citing papers per year: 2025: 2; 2026: 1.

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  1. 2025DocPrism: Multi-lingual Detection of Incorrectness Inconsistencies between Code and Documentation
  2. 2026Bidirectional Empowerment of Metamorphic Testing and Large Language Models: A Systematic Survey
  3. 2026Inferring Code Correctness from Specification
  4. 2025Demystifying Errors in LLM Reasoning Traces: An Empirical Study of Code Execution Simulation
  5. 2025DISTINCT: A Description-Guided Branch-Consistency Analysis Framework for Non-Regressive Test Case Generation ↗
  6. 2026On the risk of coding before testing: An empirical study on LLM-based test generation workflow
  7. 2025LLM Assisted Coding with Metamorphic Specification Mutation Agent
  8. 2025AugmenTest: Enhancing Tests with LLM-Driven Oracles