
This research paper proposes JFuzz, a novel approach that integrates Large Language Models (LLMs) into JSON parser fuzzing to enhance bug discovery and analyze behavioral diversities among different parser implementations.


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leveraging-large-language-models-for-enhanced-json-parser-fuzzing-a-novel-approach-to-bug-discovery-and-behavioral-analysis


Leveraging Large Language Models for Enhanced JSON Parser Fuzzing: A Novel Approach to Bug Discovery and Behavioral Analysis


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Large language models (LLMs) show promise for automated program repair (APR) but are susceptible to code style variations. This research introduces MT-LAPR, a metamorphic testing framework that identifies and exploits these vulnerabilities to improve LLM-based APR robustness.


exploring-and-enhancing-the-robustness-of-llm-powered-automated-program-repair-using-metamorphic-testing


Exploring and Enhancing the Robustness of LLM-Powered Automated Program Repair Using Metamorphic Testing
