Can we find bugs using LLM-generated oracles?

Abstract

Unit testing is vital in software development. Typically, a unit test consists of a test prefix and a test oracle which captures the developer's intended behaviour. Traditional test generation tools (e.g. Randoop and Evosuite) often produce oracles that mirror the program's actual behavior rather than the expected one, limiting their ability to automatically detect bugs as users must manually verify if the generated assertions are correct. Recent approaches leverage Large Language Models (LLMs), trained on vast datasets, to generate developer-like code and test cases. Although successful in generating tests, the question of whether such LLM-generated oracles can automatically find bugs, i.e., expected software behavior, remains unanswered. We conduct a controlled experiment to answer this question, by studying LLMs on two tasks, namely, test oracle classification and generation, and assessing whether LLM oracles capture the actual or the expected behavior. The study includes test cases and oracles written by developers and automatically generated for 24 Java repositories. Our findings show that LLM-based test generation approaches mainly capture the actual program behavior making bug detection difficult. We also find that LLMs are better at generating oracles than classifying them. Notably, LLM-generated oracles have a higher fault detection potential than the Evosuite ones.