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

TL;DR

This systematic review analyzes how machine learning techniques are used to generate test oracles, highlighting current methods, evaluation metrics, and open challenges in this emerging research area.

Contribution

It provides a comprehensive overview of existing ML-based test oracle generation approaches, identifying common techniques, evaluation methods, and key open research challenges.

Findings

ML algorithms generate various oracle types, mainly expected outputs.

Supervised and semi-supervised learning are predominantly used.

Significant open challenges include data requirements and reproducibility.

Abstract

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.