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.

Machine Learning

Test Oracle

Automated Test Generation

Automated Test Oracle Generation

Författare

Afonso Fontes

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Testing, Requirements, Innovation and Psychology

Gregory Gay

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Testing, Requirements, Innovation and Psychology

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

1-10

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.

Ämneskategorier

Programvaruteknik

Systemvetenskap

Datavetenskap (datalogi)

DOI

10.1145/3472675.3473974

ISBN

9781450386265

Mer information

Senast uppdaterat

2021-11-09