Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Toward a K-means clustering approach to adaptive random testing for object-oriented software

This is a preview of subscription content, log in to check access.


  1. 1

    Duran J W, Ntafos S C. An evaluation of random testing. IEEE Trans Softw Eng, 1984, 10: 438–444

  2. 2

    Hamlet D, Taylor R N. Partition testing does not inspire confidence. IEEE Trans Softw Eng, 1990, 16: 1402–1411

  3. 3

    Chen T Y, Leung H, Mak I K. Adaptive random testing. In: Proceedings of Asian Computing Science Conference, Chiang Mai, 2005. 320–329

  4. 4

    Chen T Y, Kuo F C, Merkel R G, et al. Adaptive random testing: the ART of test case diversity. J Syst Softw, 2010, 83: 60–66

  5. 5

    Ciupa I, Leitner A, Oriol M, et al. ARTOO: adaptive random testing for object-oriented software. In: Proceedings of International Conference on Software Engineering, Leipzig, 2008. 71–80

  6. 6

    Chen J, Kuo F C, Chen T Y, et al. A similarity metric for the inputs of OO programs and its application in adaptive random testing. IEEE Trans Rel, 2017, 66: 373–402

  7. 7

    Chan K P, Chen T Y, Towey D. Forgetting test cases. In: Proceedings of Annual International Computer Software and Applications Conference, Chicago, 2006. 485–494

  8. 8

    Hartigan J A, Wong M A. Algorithm AS 136: a K-means clustering algorithm. J Royal Stat Soc C-Appl, 1979, 28: 100–108

  9. 9

    Stanković R S, Falkowski B J. The Haar wavelet transform: its status and achievements. Comput Electr Eng, 2003, 29: 25–44

Download references


This work was supported in part by National Natural Science Foundation of China (Grant Nos. U1836116, 61762040, 61872167).

Author information

Correspondence to Jinfu Chen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Zhou, M., Tse, T.H. et al. Toward a K-means clustering approach to adaptive random testing for object-oriented software. Sci. China Inf. Sci. 62, 219105 (2019).

Download citation