Article

Neural networks based automated test oracle for software testing

Authors:

Lin YaoAuthors Info & Claims

ICONIP'06: Proceedings of the 13th international conference on Neural information processing - Volume Part III

Pages 498 - 507

Published: 03 October 2006 Publication History

Abstract

A lot of test cases must be executed in statistical software testing to simulate the usage of software. Therefore automated oracle is needed to automatically generate the expected outputs for these test cases and compare the actual outputs with them. An attempt has been made in this paper to use neural networks as automated test oracle. The oracle generates the approximate output that is close to expected output. The actual output from the application under test is then compared with the approximate output to validate the correctness. By the method, oracle can be automated. It is of potential application in software testing.

References

[1]

Sayre, K.: Improved techniques for software testing based on Markov chain usage models, PhD. thesis, University of Tennessee, Knoxville, USA (1999)

Digital Library

[2]

Bertolini, C., Farina, A.G., Fernandes, P., Oliveira, F.M.: Test case generation using stochastic automata networks: quantitative analysis, In: Proc. of the second International Conf. on Software Engineering and Formal Methods, IEEE Press (2004) 251-260

Digital Library

[3]

Beyer, M., Dulz, W., Zhen, F.: Automated TTCN-3 test case generation by means of UML sequence diagrams and Markov chains, In: Proc. of the 12th Asian Test Symposium, Piscataway : IEEE Press (2003) 102-105

[4]

Peters, D., Parnas, D.L.: Generating a test oracle from program documentation, In: Proc. of the International Symposium on Software Testing and Analysis (1994) 58-65

Digital Library

[5]

Bousquet, L., Ouabdesselam, F., Richier, J., Zuanon, N.: Lutess: a specification-driven testing environment for synchronous software, In: Proc. of the 21th International Conf. on Software Engineering, ACM Press (1999) 267-276

Digital Library

[6]

Dillon, L.K., Ramakrishna, Y.S.: Generating oracles from your favorite temporal logic specifications, In: Proc. of the 4th ACM SIGSOFT Symposium on the Foundations of Software Engineering, ACM Software Engineering Notes, vol.21 (1996) 106-117

Digital Library

[7]

Schroeder, P.J., Faherty, P., Korel, B.: Generating expected results for automated blackbox testing, In: Proc. of the 17th IEEE International Conf. on Automated Software Engineering, IEEE Press (2002) 139-148

Digital Library

[8]

Ostrand, T., Anodide, A., Foster, H., Goradia, T.: A visual test development environment for GUI systems, ACM SIGSOFT Software Engineering Notes, vol.23, no.2 (1998) 82-92

Digital Library

[9]

Chen, W.K., Tsai, T.H., Chao, H.H.: Integration of specification-based and CR-based approaches for GUI testing, In: Proc. of the 19th International Conf. on Advanced Information Networking and Applications, vol.1 (2005) 967-972

Digital Library

[10]

Memon, A., Nagarajan, A. Xie, Q.: Automating regression testing for evolving GUI software, Journal of Software Maintenance and Evolution: Research and Practice, vol.17, no.1 (2005) 27-64

Digital Library

[11]

Chen, J. Subramaniam, S.: Specification-based testing for GUI-based applications, Software Quality Journal, vol.10, no.3 (2002) 205-224

Digital Library

[12]

Hierons, R.M.: Testing from a Z specification, Software Testing, Verification, and Reliability, vol.7 (1997) 19-33

[13]

McDonald, J. Strooper, P.: Translating object-Z specifications to passive test oracles, In: Proc. of the 2th International Conf. on Formal Engineering Methods, IEEE Press (1998) 165-174

Digital Library

[14]

Aggarwal, K.K., Singh, Y., Kaur, A., Sangwan, O.P.: A neural net based approach to test oracle, ACM SIGSOFT Software Engineering Notes, ACM Press, vol.29, no.3 (2004) 1-6

Digital Library

[15]

Ramamoorthy G.V., Ho S.F., Chen W.T.: On the automated generation of program test data, IEEE Trans. Software Engineering, vol.SE-2 (1976) 293-300

Digital Library

[16]

Chen, T., Chen, H.: Approximations of continuous functionals by neural networks with application to dynamic systems, IEEE Trans. Neural Networks, vol.4, no.6 (1993) 910-918

Digital Library

[17]

Chen, D.S., Jain, R.C.: A robust back propagation learning algorithm for function approximation, IEEE Trans. Neural Networks, vol.5, no.3 (1994) 467-479

Digital Library

[18]

Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, second edition, John Wiley & Sons (2001)

Digital Library

[19]

Fausett, L.: Fundamentals of neural networks: architectures, algorithms, and application, Prentice Hall: Englewood Cliffs, New Jersey (1994)

Digital Library

Cited By

Wang XAli SArrieta AArcaini PArratibel Md'Amorim M(2024)Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators’ Software in an Industrial ContextCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663859(399-410)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663859
Singh Y(2015)Automated Expected Output GenerationACM SIGSOFT Software Engineering Notes10.1145/2830719.283072640:6(1-5)Online publication date: 11-Nov-2015
https://dl.acm.org/doi/10.1145/2830719.2830726
Shahamiri SKadir WIbrahim SHashim S(2011)An automated framework for software test oracleInformation and Software Technology10.1016/j.infsof.2011.02.00653:7(774-788)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.infsof.2011.02.006

Neural networks based automated test oracle for software testing
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ICONIP'06: Proceedings of the 13th international conference on Neural information processing - Volume Part III

October 2006

1215 pages

ISBN:3540464840

Editors:
Irwin King
Chinese University of Hong Kong, Department of Computer Science and Engineering, Hong Kong
,
Laiwan Chan
Chinese University of Hong Kong, Department of Computer Science and Engineering, Hong Kong
,
Jun Wang
Chinese University of Hong Kong, Department of Automation and Computer-Aided Engineering, Hong Kong
,
DeLiang Wang
Ohio State University, Department of Computer Science and Engineering, Ohio

Sponsors

APNNA: Asia Pacific Neural Network Assembly

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 03 October 2006

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Cited By

Wang XAli SArrieta AArcaini PArratibel Md'Amorim M(2024)Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators’ Software in an Industrial ContextCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663859(399-410)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663859
Singh Y(2015)Automated Expected Output GenerationACM SIGSOFT Software Engineering Notes10.1145/2830719.283072640:6(1-5)Online publication date: 11-Nov-2015
https://dl.acm.org/doi/10.1145/2830719.2830726
Shahamiri SKadir WIbrahim SHashim S(2011)An automated framework for software test oracleInformation and Software Technology10.1016/j.infsof.2011.02.00653:7(774-788)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.infsof.2011.02.006

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