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About this paper
Facundo Molina, Alessandra Gorla
This paper examines the feasibility of leveraging Large Language Models (LLMs) for automated test oracle generation, covering assertions, contracts, and metamorphic relations. It outlines potential application strategies and identifies critical challenges such as ensuring oracle quality and mitigating data leakage in evaluation, offering specific recommendations for future research.
Key insights
- LLMs possess the capability to generate various types of test oracles, but their output quality is not inherently guaranteed, necessitating external assurance mechanisms.
- False positives in automatically generated oracles are a primary concern, as they can erode trust in the testing process and lead to inefficient debugging efforts.
- Data leakage from LLM training datasets can significantly skew evaluation results, requiring careful methodology and diverse, fresh datasets to accurately assess performance.
Summary by alphaXiv.
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