Biophysical Reviews

, Volume 11, Issue 1, pp 123–125 | Cite as

Towards testing big data analytics software: the essential role of metamorphic testing

  • Zhiyi Zhang
  • Xiaoyuan XieEmail author


In the rapidly growing field of big data analysis, scientists from numerous domains such as computer science and biology are constantly challenged by an unprecedented amount of data. While many software programs have been constructed to support processing and analyzing continuous information flow, one under-appreciated challenge in this field is software quality assurance of these big data software platforms. Metamorphic testing, which was proposed to alleviate the oracle problem in the software engineering community, has become an effective approach for software verification and validation. Recent years, we have witnessed successful applications of metamorphic testing in a variety of domains, ranging from bioinformatics to deep learning. In this letter, we review some main applications of metamorphic testing on big data and present visions for the challenges in future research.


Software engineering Metamorphic testing Big data software 


Funding information

This work is supported by National Key R&D Program of China (2018YFB1003901), and the National Natural Science Foundation of China (61572375, 61772263).

Compliance with ethical standards

Conflict of interest

Zhiyi Zhang declares that she has no conflict of interest. Xiaoyuan Xie declares that she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Chen TY, Cheung SC, Yiu SM (1998) Metamorphic testing: a new approach for generating next test cases. Tech. rep., Technical Report HKUST-CS98-01, Department of Computer Science. Hong Kong University of Science and Technology, Hong KongGoogle Scholar
  2. Chen TY, Ho JW, Liu H, Xie X (2009) An innovative approach for testing bioinformatics programs using metamorphic testing. BMC Bioinf 10(1):24. CrossRefGoogle Scholar
  3. Chen TY, Kuo FC, Liu H, Poon PL, Towey D, Tse T, Zhou ZQ (2018) Metamorphic testing: a review of challenges and opportunities. ACM Comput Surv 51(1):4:1–4:27CrossRefGoogle Scholar
  4. Ding J, Kang X, Hu X (2017) Validating a deep learning framework by metamorphic testing. In: Proceedings of the 2nd international workshop on metamorphic testing. IEEE, pp 28–34.
  5. Marx V (2013) Biology: the big challenges of big data. Nature 498:255–260CrossRefGoogle Scholar
  6. Murphy C, Kaiser G, Hu L, Wu L (2008) Properties of machine learning applications for use in metamorphic testing. In: Proceedings of the 20th international conference on software engineering and knowledge engineering, pp 867–872Google Scholar
  7. Otero CE, Peter A (2015) Research directions for engineering big data analytics software. IEEE Intell Syst 30(1):13–19. CrossRefGoogle Scholar
  8. Pullum LL, Ozmen O (2012) Early results from metamorphic testing of epidemiological models. In: ASE/IEEE International Conference on BioMedical Computing (BioMedCom)(BIOMEDCOM), pp 62–67.
  9. Sadi MS, Kuo FC, Ho JWK, Charleston MA, Chen TY (2011) Verification of phylogenetic inference programs using metamorphic testing. J Bioinform Comput Biol 9(6):729–747CrossRefGoogle Scholar
  10. Segura S, Fraser G, Sanchez AB, Ruiz-Cortés A (2016) A survey on metamorphic testing. IEEE Trans Softw Eng 42(9):805–824. CrossRefGoogle Scholar
  11. Tian Y, Pei K, Jana S, Ray B (2018) Deeptest: automated testing of deep-neural-network-driven autonomous cars. In: Proceedings of the 40th international conference on software engineering. ACM, pp 303–314, DOI, (to appear in print)
  12. Weyuker EJ (1982) On testing non-testable programs. Comput J 25(4):465–470CrossRefGoogle Scholar
  13. Xie X, Ho JW, Murphy C, Kaiser G, Xu B, Chen TY (2011) Testing and validating machine learning classifiers by metamorphic testing. J Syst Softw 84(4):544–558CrossRefGoogle Scholar
  14. Zhang M, Zhang Y, Zhang L, Liu C, Khurshid S (2018) Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems. In: Proceedings of the 33rd ACM/IEEE international conference on automated software engineering. ACM, pp 132–142, (to appear in print),

Copyright information

© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina

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