[1] Jiang Yuting, Li Bixin,. Novel technique for cost reduction in mutation testing [J]. Journal of Southeast University (English Edition), 2011, 27 (1): 17-21. [doi:10.3969/j.issn.1003-7985.2011.01.004]

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

27

Issue:

2011 1

Page:

17-21

Research Field:

Computer Science and Engineering

Publishing date:

2011-03-30

Info

Title:

Novel technique for cost reduction in mutation testing

Author(s):

Jiang Yuting;  Li Bixin

School of Computer Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

mutation testing;  mutation distance;  sample learning;  execution cost;  test case selection

PACS:

TP311

DOI:

10.3969/j.issn.1003-7985.2011.01.004

Abstract:

Aimed at the problem of expensive costs in mutation testing which has hampered its wide use, a technique of introducing a test case selection into the process of mutation testing is proposed. For each mutant, a fixed number of test cases are selected to constrain the maximum allowable executions so as to reduce useless work. Test case selection largely depends on the degree of mutation. The mutation distance is an index describing the semantic difference between the original program and the mutated program. It represents the percentage of effective test cases in a test set, so it can be used to guide the selection of test cases. The bigger the mutation distance is, the easier it is that the mutant will be killed, so the corresponding number of effective test cases for this mutant is greater. Experimental results suggest that the technique can remarkably reduce execution costs without a significant loss of test effectiveness.

References:

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[5] Yue J, Harman M. An analysis and survey of the development of mutation testing[R]. London: Center for Research on Evolution, Search and Testing(CREST), 2009.
[6] Wong W E, Mathur A P. Reducing the cost of mutation testing: an empirical study[J]. The Journal of Systems and Software, 1995, 31(3):185-196.
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[8] Zhang L, Hou S S, Hu J J, et al. Is operator-based mutant selection superior to random mutant selection? [C]//Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering. New York, NY, USA:ACM, 2010:435-444.
[9] Offutt A J, Lee A, Rothermel G, et al. An experimental determination of sufficient mutant operators[J]. ACM Transactions on Software Engineering and Methodology, 1996, 5(2): 99-118.
[10] Offutt A J, Rothermel G, Zapf C. An experimental evaluation of selective mutation[C]//Proceedings of the 15th International Conference on Software Engineering. Baltimore, MD, USA, 1993:100-107.
[11] DeMillo R A, Offutt A. Constraint-based automatic test data generation[J]. IEEE Transactions on Software Engineering, 1991, 17(9):900-910.

Memo

Memo:

Biographies: Jiang Yuting(1986—), female, graduate; Li Bixin(corresponding author), male, doctor, professor, bx.li@seu.edu.cn.
Foundation items: The National High Technology Research and Development Program of China(863 Program)(No.2008AA01Z113), the National Natural Science Foundation of China(No. 60773105, 60973149).
Citation: Jiang Yuting, Li Bixin. Novel technique for cost reduction in mutation testing[J].Journal of Southeast University(English Edition), 2011, 27(1):17-21.[doi:10.3969/j.issn.1003-7985.2011.01.004]

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