Using Machine Learning to Generate Test Oracles: A Systematic Literature Review
Paper i proceeding, 2021
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
Författare
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
1-10
978-145038626-5 (ISBN)
Athens, Greece,
Context-Infused Automated Software Test Generation
Vetenskapsrådet (VR) (2019-05275), 2020-01-01 -- 2023-12-31.
Ämneskategorier
Programvaruteknik
Systemvetenskap
Datavetenskap (datalogi)
DOI
10.1145/3472675.3473974
ISBN
9781450386265