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洞見 - SoftwareTesting and Quality Assurance
Leveraging Large Language Models for Enhanced JSON Parser Fuzzing: A Novel Approach to Bug Discovery and Behavioral Analysis
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.
Exploring and Enhancing the Robustness of LLM-Powered Automated Program Repair Using Metamorphic Testing
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.
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