METAMON: Finding Inconsistencies between Program Documentation and Behavior using Metamorphic LLM Queries
February 05, 2025 Β· Declared Dead Β· π 2025 IEEE/ACM International Workshop on Large Language Models for Code (LLM4Code)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hyeonseok Lee, Gabin An, Shin Yoo
arXiv ID
2502.02794
Category
cs.SE: Software Engineering
Citations
2
Venue
2025 IEEE/ACM International Workshop on Large Language Models for Code (LLM4Code)
Last Checked
4 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted