Metamorphic Relation Generation for Physics Burnup Program Testing

doi:10.23940/ijpe.20.02.p12.297306

Abstract

Abstract:

Due to the high complexity of physics burnup calculation, its output is extremely difficult to predict. Therefore, the traditional testing methods are ineffective. Metamorphic testing (MT) is an effective method to solve the oracle problem. However, the manual identification of metamorphic relation (MRs) significantly hinders its application. We propose a novel MR automatic generation framework using gene expression programming (GEP), where the MR identification problem is converted into a symbolic expression regression problem. Specifically, the explicit MRs and the domain-related function operators are inferred by static analysis from the nuclear background knowledge. The MR is encoded as the gene expression by using function operators, symbolic variables, and constants. After a series of genetic operations, we obtain the implicit MRs by decoding the optimal solution of the expression. Moreover, the correctness of each MR is evaluated according to reliability verification, logical conflict, and logical redundancy. The effectiveness is automatically verified by comparing the consistency of output from a group of burnup calculation programs.

Key words: metamorphic relation automatic generation, GEP, homogeneous burnup equation

Meng Li, Lijun Wang, Shiyu Yan, and Xiaohua Yang. Metamorphic Relation Generation for Physics Burnup Program Testing [J]. Int J Performability Eng, 2020, 16(2): 297-306.

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References 35.

1.	L. Meng, W. Li-Jun, L. Yu-Yan,Y. Xiao-Hua,“Research on Testing Adequacy Criterion of Reactor Physics Code of NESTOR,” in Proceedings of the 2017 25th International Conference on Nuclear Engineering, p. 4, Shanghai, China, July 2017.
2.	M. Li, X. H. Yang, S. Y. Yan, H. Liu,C. H. Liu, “An automatic generation tool for unit test case based on dynamic symbolic execution,” inProceedings of the 2019 27th International Conference on Nuclear Engineering, pp.6, Tsukuba, Ibaraki, Japan, May 2019.
3.	T. Y. Chen, T. H. Tse,Z. Q. Zhou, “Fault-based testing without the need of oracles,” Information and Software Technology, vol. 45, no. 1, pp. 1-9, 2003.
4.	L. I.Manolache and D. G. Kourie, “Software testing using model programs,” Software - Practice and Experience, vol. 31, no. 13, pp. 1211-1236, 2001.
5.	D. S. Rosenblum, “A Practical Approach to Programming With Assertions,” IEEE Transactions on Software Engineering, vol. 21, no. 1, pp. 19-31, 1995.
6.	T. Chen, S. Cheung,S. Yiu, “Metamorphic testing: a new approach for generating next test cases,” HongKong, 1998.
7.	P. L. Poon, S. F. Tang, T. H. Tse,T. Y. Chen, “CHOC’LATE: A framework for specification-based testing,” Communications of the ACM, vol. 53, no. 4, pp. 113-118, 2010.
8.	T. Y. Chen, P. L. Poon,X. Xie, “METRIC: METamorphic Relation Identification based on the Category-choice framework,” Journal of Systems and Software, vol. 116, pp. 177-190, Jun. 2016.
9.	H. Liu, P. L. Poon,T. Y. Chen, “Poster: Enhancing Partition Testing through Output Variation,” 2015.
10.	C.-A. Sun, A. Fu, P.-L. Poon, X. Xie, H. Liu, and T. Y. Chen, “METRIC+: A Metamorphic Relation Identification Technique Based on Input plus Output Domains,”IEEE Transactions on Software Engineering, pp. 1-1, Aug. 2019.
11.	Z. Lu, Q. Li, D. Liu, X. Chai, H. Fang,Z. Gong, “Engineering Applicability Strengthening Design and Practice of NESTOR Software Package,” Hedongli Gongcheng/Nuclear Power Engineering, vol. 39, no. 1, pp. 161-164, 2018.
12.	T. Y.Chen et al., “Metamorphic testing: A review of challenges and opportunities,” ACM Computing Surveys, vol. 51, no. 1. Association for Computing Machinery, 01-Jan-2018.
13.	V. Le, M. Afshari,Z. Su, “Compiler validation via equivalence modulo inputs,” ACM SIGPLAN Notices, vol. 49, no. 6, pp. 216-226, Jun. 2014.
14.	N. Mouha, M. S. Raunak, D. Richard Kuhn,R. Kacker, “Finding Bugs in Cryptographic Hash Function Implementations,” IEEE Transactions on Reliability, vol. 67, no. 3, pp. 870-884, Sep. 2018.
15.	C. ai Sun, Z. Wang, and G. Wang, “A property-based testing framework for encryption programs,” Frontiers of Computer Science, vol. 8, no. 3, pp. 478-489, 2014.
16.	X. Xie, J. W. K. Ho, C. Murphy, G. Kaiser, B. Xu,T. Y. Chen, “Testing and validating machine learning classifiers by metamorphic testing,” in Journal of Systems and Software, 2011, vol. 84, no. 4, pp. 544-558.
17.	Z. Q.Zhou and L. Sun, “Metamorphic testing of driverless cars,” Communications of the ACM, vol. 62, no. 3, pp. 61-67, Mar. 2019.
18.	T. Y. Chen, J. W. K.Ho, H. Liu, and X. Xie, “An innovative approach for testing bioinformatics programs using metamorphic testing,” BMC Bioinformatics, vol. 10, Jan. 2009.
19	19.“Google buys GraphicsFuzz, bringing fuzzy logic and metamorphic testing to Android graphics drivers \| Packt Hub.” [Online]. Available: https://hub.packtpub.com/google-buys-graphicsfuzz-bringing-fuzzy-logic-and-metamorphic-testing-to-android-graphics-drivers/. [Accessed: 19-Dec-2019].
20.	X. H.Yang et al., “Verification Method of Metamorphic Test on Neutron Diffusion Program Based on Benchmark Problem,” Yuanzineng Kexue Jishu/Atomic Energy Science and Technology, vol. 51, no. 7, pp. 1239-1243, Jul. 2017.
21.	H. Song, D. Ruihao, L. Hui,Y. Yi, “Research on metamorphic testing of slope and aspect calculating programs,” Journal of Computer Applications, vol. 33, no. 06, pp. 1657-1745, 2013.
22.	Q. Shuting and Z. Zhixiang, “Metamorphic Testing of Ordinary Differential Equations Program,” Ship Electronic Engineering, vol. 36, no. 12, pp. 29-33, 2016.
23.	S. Yan, X. Yang, G. Cheng,H. Liu,“Richardson Extrapolation-Based Verification Method of Scientific Calculation Program without the Oracles: A Case Study,” Mathematical Problems in Engineering, vol. 2019, 2019.
24.	C. Murphy, “Metamorphic Testing Techniques to Detect Defects in Applications without Test Oracles,” 2010.
25.	S. Yan, X. Yang, M. Li, H. Liu,Z. Liu, “Research of Testing for Scientific Computing Software in the Area of Nuclear Power Based on Metamorphic Testing,” in Proceedings of The 20th Pacific Basin Nuclear Conference, Springer Singapore, 2017, pp. 501-512.
26.	T. Y. Chen, J. Feng,T. H. Tse, “Metamophic testing of programs on partial differential equations: A case study,” inProceedings - IEEE Computer Society’s International Computer Software and Applications Conference, 2002, pp. 327-333.
27.	H. Xuegang and Z. Bing, “Test approach for the program of clusters based on metamorphic relations,” Journal of Electronic Measurement and Instrument, vol. 25, no. 8, pp. 688-694, 2011.
28.	U. Kanewala, J. M. Bieman,A. Ben-Hur, “Predicting metamorphic relations for testing scientific software: A machine learning approach using graph kernels,” Software Testing Verification and Reliability, vol. 26, no. 3, pp. 245-269, May 2016.
29.	K. Rahman and U. Kanewala, “Predicting Metamorphic Relations for Matrix Calculation Programs,” inProceedings - International Conference on Software Engineering, 2018, pp. 10-13.
30.	P. Zhang, X. Zhou, P. Pelliccione,H. Leung, “RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network,” IEEE Access, vol. 5, pp. 21791-21805, Oct. 2017.
31.	J. Zhanget al., “Search-based inference of polynomial metamorphic relations,” in ASE 2014 - Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, 2014, pp. 701-712.
32.	M. D. Ernst, J. Cockrell, W. G. Griswold,D. Notkin, “Dynamically discovering likely program invariants to support program evolution,” IEEE Transactions on Software Engineering, vol. 27, no. 2, pp. 99-123, Feb. 2001.
33.	C. Ferreira, “Gene Expression Programming: a New Adaptive Algorithm for Solving Problems,” Complex Systems, vol. 13, no. 2, pp. 87-129, Feb. 2001.
34.	F. Candida, Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, vol. 21. Springer, 2006.
35.	J. Zhong, L. Feng,Y. S. Ong, “Gene Expression Programming: A Survey,” IEEE Computational Intelligence Magazine, vol. 12, no. 3. Institute of Electrical and Electronics Engineers Inc., pp. 54-72, 01-Aug-2017.