Using Machine Learning to Generate Test Oracles: A Systematic Literature Review
Paper in 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

University of Gothenburg

Gregory Gay

University of Gothenburg

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

Swedish Research Council (VR) (2019-05275), 2020-01-01 -- 2023-12-31.

Subject Categories

Software Engineering

Information Science

Computer Science





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