Using Machine Learning to Generate Test Oracles: A Systematic Literature Review
Paper i proceeding, 2021

Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field.

Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and - most commonly - expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata - including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employed - and how they are applied - the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field.

Automated Test Oracle Generation

Automated Test Generation

Machine Learning

Test Oracle


Afonso Fontes

Göteborgs universitet

Gregory Gay

Göteborgs universitet

TORACLE 2021 - Proceedings of the 1st International Workshop on Test Oracles, co-located with ESEC/FSE 2021

978-145038626-5 (ISBN)

TORACLE 2021: Proceedings of the 1st International Workshop on Test Oracles
Athens, Greece,

Context-Infused Automated Software Test Generation

Vetenskapsrådet (VR) (2019-05275), 2020-01-01 -- 2023-12-31.




Datavetenskap (datalogi)





Mer information

Senast uppdaterat