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International Journal of Scientific & Technology Research

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IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Artificial Neural Network Approach For Software Product Line Testing

[Full Text]

 

AUTHOR(S)

Ashish Saini, Rajkumar, Satendra Kumar

 

KEYWORDS

Testing, Software Product Line, Artificial Neural Network, Test Cases, Feature Model, Product Line, Configurations.

 

ABSTRACT

Software Product Line Testing (SPLT) is an immensely important task because it confirms the validity of a product while it is a time consuming and costly process. To minimize the time and check the validity confirmation of a product, an efficient method is required to decide whether a product of an SPL is faulty or not. A tester can decrease the actual cost of testing and its maintenance cost as well as time by using such efficient method. In this paper, we concentrate on an idea, where we use an Artificial Neural Network (ANN) technique, which works to test a software product line. In this technique, the backpropagation algorithm is used to train a Neural Network (NN) based on the set of test cases of the product’s actual version. The trained network treats as a black-box testing approach, in which two parts are presented for an algorithm. SPL product’s validity is measured by the distance between actual and faulty outputs.

 

REFERENCES

[1] A. Saini, Rajkumar, and S. Kumar, “A Systematic Literature Survey on Software Product Line Testing,” Int. J. Res. Anal. Rev., Vol. 6, No. 1, Pp. 253–262, 2019.
[2] S. Kumar and Rajkumar, “Test Case Prioritization Techniques for Software Product Line : A Survey,” Int. Conf. Comput. Commun. Autom., pp. 884–889, 2016.
[3] Z. Akbari, S. Khoshnevis, and M. Mohsenzadeh, “A Method For Prioritizing Integration Testing in Software Product Lines Based on Feature Model,” Int. J. Softw. Eng. Knowl. Eng., Vol. 27, No. 4, Pp. 575–600, 2017.
[4] E. Engström and P. Runeson, “Software Product Line Testing - A Systematic Mapping Study,” Inf. Softw. Technol., Vol. 53, No. 1, Pp. 2–13, 2011.
[5] I. D. C. Machado, J. D. Mcgregor, Y. C. Cavalcanti, and E. S. De Almeida, “On Strategies For Testing Software Product Lines: A Systematic Literature Review,” Inf. Softw. Technol., Vol. 56, No. 10, Pp. 1183–1199, 2014.
[6] E. Parveen Kumar and E. Pooja Sharma, “Artificial Neural Networks-A Study,” Int. J. Emerg. Eng. Res. Technol., Vol. 2, No. 2, Pp. 143–148, 2014.
[7] C. R. Arjun and A. Kumar, “Artificial Neural Network-Based Estimation of Peak Ground Acceleration,” Iset J. Earthq. Technol. Pap. No, Vol. 501, No. 1, pp. 19–28, 2009.
[8] David Kriesel, A Brief Intoduction to Neural Networks,pp 3-12. 2001.
[9] J. C. Dueñas, J. Mellado, R. Cerón, J. L. Arciniegas, J. L. Ruiz, and R. Capilla, “Model Driven Testing in Product Family Context,” 2004.
[10] L. Jin-Hua, L. Qiong, and L. Jing, “The W-Model For Testing Software Product Lines,” Int. Symp. Comput. Sci. Comput. Technol., 2008.
[11] J. D. Mcgregor, “Testing A Software Product Line,” Tech. Report, Clemson University, USA, 2001.
[12] J. M. Ferreira, S. R. Vergilio, and M. Quinaia, “Software Product Line Testing Based on Feature Model Mutation,” Int. J. Soft. Eng. Knowl. Eng., Vol. 27, No. 5, Pp. 817–839, 2017.
[13] A. Bertolino and S. Gnesi, “Pluto : A Test Methodology For Product Families,” Lecture Notes In Computer Science, Pp. 181–197, 2004.
[14] M. A. M and R. Moawad, “An Approach For Requirements Based Software Product Line Testing,” In International Conference On Informatics and Systems (Infos), 2010.
[15] R. Patil and V. C. Prakash, “Neural Network Based Approach For Improving Combinatorial Coverage in Combinatorial Testing Approach,” vol. 96, no. 20, pp. 6677–6687, 2018.
[16] C. K̈astner, T. Thum, et al., “FeatureIDE: A Tool Framework for Feature-Oriented Software Development,” Proc. - Int. Conf. Softw. Eng., no. May 2014, pp. 611–614, 2009.
[17] A. Saini, Rajkumar, and S. Kumar, “Software Product Line Configurations Generation using Different Types of Tools – A Comparison,” Int. J. Comput. Sci. Eng., vol. 6, no. 4, pp. 105–109, 2018.
[18] https://becominghuman.ai/artificial-neuron-networks-basics-introduction-to-neural-networks-3082f1dcca8c
[19] O. M. Abbas, “Neural Networks in Business Forecasting“, Int. J. Comp., Pp.114-128, ISSN 2307-4523, 2005.