Do LLMs Generate Useful Test Oracles? An Empirical Study with an Unbiased Dataset

Davide Molinelli; Luca Di Grazia; Alberto Martin-Lopez; Michael D. Ernst; Mauro Pezzè

doi:10.18420/se2026_11

January 1, 2026Open Access

Do LLMs Generate Useful Test Oracles? An Empirical Study with an Unbiased Dataset

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Key Points

The research aims to evaluate whether large language models can produce effective test oracles.
Conducted an empirical study using an unbiased dataset
Assessed the performance of LLMs in generating test oracles
Presented findings at a major conference on automated software engineering.
Demonstrated potential usefulness of LLM-generated test oracles
Highlighted areas where LLMs may fall short in effectiveness.

Implication

An empirical study assesses LLMs' ability to generate useful test oracles, suggesting implications for automated software engineering.

Abstract

In this article, we summarize our paper Do LLMs Generate Useful Test Oracles? An Empirical Study with an Unbiased Dataset [Mo25], which has been recently accepted for presentation at the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE).

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Cite This Study

Molinelli et al. (2026) studied this question.

synapsesocial.com/papers/698828010fc35cd7a88472aa https://doi.org/10.18420/se2026_11

View Full Paper

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Do LLMs Generate Useful Test Oracles? An Empirical Study with an Unbiased Dataset

Key Points

Implication

Abstract

Discussion

Cite This Study

Do LLMs Generate Useful Test Oracles? An Empirical Study with an Unbiased Dataset

Key Points

Implication

Abstract

Discussion

Cite This Study

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