research-article

Radial basis function neural network based approach to test oracle

Authors:

Om Prakash Sangwan,

Pradeep Kumar Bhatia,

Yogesh SinghAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 36, Issue 5

Pages 1 - 5

https://doi.org/10.1145/2020976.2020992

Published: 30 September 2011 Publication History

Abstract

Software testing is an important discipline, and consumes significant amount of effort. A proper strategy is required to design and generate test cases systematically and effectively. In this paper automated software test case generation with Radial Basis Function Neural Network (RBFNN) has been proposed and empirically validated with the help of a case study and compared with other techniques of soft computing. Experimental results show that RBFNN is one of the best technique for automated test case generation.

References

[1]

Acree A.T., Budd T.A., DeMillo R.A., Lipton R.J., Sayward F.G. 1979. Mutation Analysis. School of Information and Computer Science. Georgia Institute Technology, Atlanta, tech. Rep. GIT-ICS-79/08.

[2]

Anderson C., Von Mayrhauser, A. Mraz, R. 1995. On the Use of Neural Networks to Guide Software Testing Activities. Test Conference, 1995. Proceedings International, 21-25 Oct, pp 720--729.

Digital Library

[3]

Aggarwal K.K., Yogesh Singh, 2001.Software Engineering Programs, Documentation Operating Procedure. New Age International Publishers (2001).

[4]

Aggarwal K.K., Singh Yogesh, Kaur Arvinder, Sangwan O. P 2004. A Neural Net Based Approach to Test Oracle. ACM SIGSOFT, Vol. 29, No.4, (May 2004).

Digital Library

[5]

Aggarwal K.K., Singh Yogesh, Kaur Arvinder, 2005. Empirical Studies on Application of Neural Networks as Approximation Oracle. Journal of Computer Science 1(3): pp 341--345.

[6]

Alippi, C., V. Piuri, F. Scotti, 2001. Accuracy versus Complexity in RBF Neural Networks. IEEE Instrumentation and Measurement Magazine 4, pp 32--36.

[7]

Beizer B.: Software Testing Techniques, 1990. Van Nostrand Reinhold, New York.

Digital Library

[8]

Clarke L.A., Richardson D.J. 1983. The Application of Error Sensitive Testing Strategies to Debugging. In Symposium of High-Level debugging, ACM SIGSOFT/SIGPLAN, pp 45--52.

Digital Library

[9]

Collofello J.S. 1988. Introduction to Software Verification and Validation. SEI-CM-13-1.1, Software Engineering Institute, Pittsburgh, P.A., USA.

[10]

DeMillo R.A., Lipton R.J., Sayward F. G. 1978. Hints on Test Data Selection: Help for Practicing Programmers. IEEE Computer, Vol. C- 11: pp 34--41.

Digital Library

[11]

Doungsa-ard C., K. Dahal, A. Hossain, 2006. AI Based Framework for Automatic Test Data Generation. IWS06. http://eastwest.inf.brad.ac.uk/document/publication/Doungsa-ard-IWS06%20.pdf

[12]

Duda R.O., P.E. Hart, D.G. Stork, 2001. Pattern Classification. 2nd Edition, John Wiley and Sons.

Digital Library

[13]

Finnie G.R., Wittig G.E., 1996. AI Tools for Software Development Effort Estimation. IEEE Transaction on Software Engineering, pp 346--353.

Digital Library

[14]

Han J., Kamber M., 2001. Data Mining: Concepts and Techniques. Harchort India Private Limited.

Digital Library

[15]

Hopkins W.G. 2003. A New View of statistics. Sports Science.

[16]

Howden William E., Eichhorst Peter, 1978. Proving Properties of Programs from Program Traces. In Tutorial: Software Testing and Validation Techniques: E Miller and W.E. howden (eds.0. new York: IEEE Computer Society Press.

[17]

Karayiannis N.B., G.W. Mi., 1997. Growing Radial Basis Neural Network: Merging Supervised and Unsupervised Learning with Network Growth Techniques. IEEE Transaction on Neural Networks, 8, pp 1492--1506.

Digital Library

[18]

Li Y.H., S. Qiang, X.Y. Zhuang, O. Kayank, 2006. Robust and Adaptive Back Stepping Control for Nonlinear Systems Using RBF Neural Network. IEEE Transaction on Neural Networks, 15, pp 693--701 (2004).decisions. J. Syst. Softw. 79, 5 (May. 2006), 577--590. DOI=http://dx.doi.org/10.1016/j.jss.2005.05.030.

Digital Library

[19]

Manna Zohar, Waldinger Richard, 1978. The Logic of Computer Programming. IEEE Transactions on Software Engineering SE- 4(3):199--229.

Digital Library

[20]

Mayrhauser A.V., DeMillo A., Offutt A.J., 1991. Constraint-Based Automatic Test Data Generation. IEEE Transactions on Software Engineering SE-17, pp 900--910.

Digital Library

[21]

Mayrhauser A. Von, Walls J., Mraz R. 1994. Testing Applications Using Domain Based Testing and Sleuth. Proceedings of the Fifth International Software Reliability Engineering Symposium, Monterey, November 1994, pp 206--215.

[22]

Mayrhauser A. Von, Anderson C., Mraz R. 1995. Using A Neural Network To Predict Test Case Effectiveness. Proceedings IEEE Aerospace Applications Conference, Snowmass CO.

[23]

Nebut Clémentine, Franck Fleurey, Yves Le Traon, Jean-Marc Jézéquell 2006. Automatic Test Case Generation: A Use Case Driven Approach. IEEE Transaction on Software Engineering, Vol. 32, No.3, pp 140--155.

Digital Library

[24]

Pressman R.S. 1977. Software Engineering: A Practitioners Approach. McGraw Hill, New York.

Digital Library

[25]

Rajan Ajitha, 2006. Automated Requirements Based Test Case Generation. FSE 06, November 5-11, Portland, Oregon, USA.

[26]

Ramamoorthy G.V., Ho S.F., Chen W.T. 1976. On the Automated Generation of Program Test Data. IEEE Transactions on Software Engineering Vol. SE-2, pp 293--300.

Digital Library

[27]

Roy A., S. Govil, R. Miranda, 1997. A Neural Network Learning Theory and a Polynomial Time RBF Algorithm. IEEE Transaction on Neural Networks, 8, pp 1301--1313.

Digital Library

[28]

Rumelhart D. et al. 1986. Parallel Distributed Processing: Explorations in the Microstructures of Cognition. MIT Press, Mass.

Digital Library

[29]

Wang L. et al 2004. Generating test cases from UML activity diagram based on Gray-box method. In Software Engineering Conference, 11th Asia-Pacific.

Digital Library

[30]

Ying Lu, Mao Ye, 2007. Oracle Model Based on RBF Neural Networks for Automated Software Testing. Information Technology Journal, 6(3), pp 469--474.

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
Kamaraj KLanitha BKarthic SN. Senthil Prakash PMahaveerakannan R(2023)A Hybridized Artificial Neural Network for Automated Software Test OracleComputer Systems Science and Engineering10.32604/csse.2023.02970345:2(1837-1850)Online publication date: 2023
https://doi.org/10.32604/csse.2023.029703
Fontes AGay G(2023)The integration of machine learning into automated test generation: A systematic mapping studySoftware Testing, Verification and Reliability10.1002/stvr.184533:4Online publication date: 2-May-2023
https://doi.org/10.1002/stvr.1845
Show More Cited By

Index Terms

Radial basis function neural network based approach to test oracle
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues

Recommendations

A neural net based approach to Test Oracle

In this paper an attempt has been made to explore the possibility of the usage of artificial neural networks as Test Oracle. The triangle classification problem has been used as a case study. Results for the usage of unsupervised artificial networks ...
Multiplicative neuron model artificial neural network based on Gaussian activation function

Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron ...
A weight optimized artificial neural network for automated software test oracle
Abstract
Software testing has its main goal as designing new test case sets in a manner in which it is able to depict its maximum faults. As soon as these test cases have been designed, Oracle software provides a method in which the software has to behave ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 36, Issue 5

September 2011

160 pages

ISSN:0163-5948

DOI:10.1145/2020976

Issue’s Table of Contents

Copyright © 2011 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 September 2011

Published in SIGSOFT Volume 36, Issue 5

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
33
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

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
Kamaraj KLanitha BKarthic SN. Senthil Prakash PMahaveerakannan R(2023)A Hybridized Artificial Neural Network for Automated Software Test OracleComputer Systems Science and Engineering10.32604/csse.2023.02970345:2(1837-1850)Online publication date: 2023
https://doi.org/10.32604/csse.2023.029703
Fontes AGay G(2023)The integration of machine learning into automated test generation: A systematic mapping studySoftware Testing, Verification and Reliability10.1002/stvr.184533:4Online publication date: 2-May-2023
https://doi.org/10.1002/stvr.1845
Gartziandia AArrieta AAyerdi JIllarramendi MAgirre ASagardui GArratibel M(2022)Machine learning‐based test oracles for performance testing of cyber‐physical systems: An industrial case study on elevators dispatching algorithmsJournal of Software: Evolution and Process10.1002/smr.246534:11Online publication date: 25-May-2022
https://doi.org/10.1002/smr.2465
Fontes AGay GJahangirova GTerragni V(2021)Using machine learning to generate test oracles: a systematic literature reviewProceedings of the 1st International Workshop on Test Oracles10.1145/3472675.3473974(1-10)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1145/3472675.3473974
Manju Bhatia P(2021)Dynamic Reusability Measurement Using Machine Learning Algorithms in Object-Oriented EnvironmentIntelligent Computing and Communication Systems10.1007/978-981-16-1295-4_40(393-399)Online publication date: 22-Jun-2021
https://doi.org/10.1007/978-981-16-1295-4_40
Sharma DChandra P(2017)Applicability of Soft Computing and Optimization Algorithms in Software Testing and Metrics – A Brief ReviewProceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)10.1007/978-3-319-60618-7_53(535-546)Online publication date: 19-Aug-2017
https://doi.org/10.1007/978-3-319-60618-7_53
Mann MSangwan OTomar P(2015)Hybrid test language processing based framework for test case optimizationCSI Transactions on ICT10.1007/s40012-015-0073-93:2-4(71-81)Online publication date: 19-Nov-2015
https://doi.org/10.1007/s40012-015-0073-9
Singhal ABansal AKumar A(2015)An approach to design test oracle for aspect oriented software systems using soft computing approachInternational Journal of System Assurance Engineering and Management10.1007/s13198-015-0402-27:1(1-5)Online publication date: 1-Dec-2015
https://doi.org/10.1007/s13198-015-0402-2
Oliveira RKanewala UNardi P(2014)Automated Test Oracles10.1016/B978-0-12-800160-8.00003-6(113-199)Online publication date: 2014
https://doi.org/10.1016/B978-0-12-800160-8.00003-6

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents