A machine learning approach to generate test oracles-Reference-Cited by-同舟云学术

A machine learning approach to generate test oracles

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the XXXII Brazilian Symposium on Software Engineering - SBES '18
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Author:

Braga Ronyérison¹,Neto Pedro Santos¹,Rabêlo Ricardo¹,Santiago José¹,Souza Matheus¹

Affiliation:

1. Federal University of Piaui, Teresina, PI, Brazil

Publisher

ACM Press

Reference40 articles.

1. Earl T Barr, Mark Harman, Phil McMinn, Muzammil Shahbaz, and Shin Yoo. 2015. The oracle problem in software testing: A survey.IEEE transactions on software engineering41, 5 (2015), 507--525.

2. Michael W Browne. 2000. Cross-validation methods.Journal of mathematical psychology44, 1 (2000), 108--132.

3. WK Chan, Shing Chi Cheung, and Karl RPH Leung. 2005. Towards a metamorphic testing methodology for service-oriented software applications. InFifth International Conference on Quality Software, 2005.(QSIC 2005).IEEE, 470--476.

4. WK Chan and TH Tse. 2013. Oracles are hardly attain'd, and hardly understood: Confessions of software testing researchers. In13th International Conference on Quality Software (QSIC), 2013. IEEE, 245--252.

5. Marcio Eduardo Delamaro, Fátima Lourdes dos Santos Nunes, and Rafael Alves Paes Oliveira. 2013. Using concepts of content-based image retrieval to implement graphical testing oracles.Software Testing, Verification and Reliability23, 3 (2013), 171--198.

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