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arXiv 2107.00906cs.SE
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Using Machine Learning to Generate Test Oracles: A Systematic Literature Review

Afonso Fontes, Gregory Gay

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Pre-print. Article accepted to 1st International Workshop on Test Oracles at ESEC/FSE 2021
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英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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