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Neural Network-Based Automated Priority Assigner

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Proceedings of the Second International Conference on Computer and Communication Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 381))

Abstract

The testing of a system starts with the crafting of test cases. Not all the test cases are, however, equally important. The test cases can be prioritized using policies discussed in the work. The work proposes a neural network model to prioritize the test cases. The work has been validated using backpropagation neural network. 200 test cases were crafted and the experiment was carried out using 2, 5, 10, 15, and 20 layers neural network. The results have been reported and lead to the conclusion that neural network-based priority analyzer can predict the priority of a test.

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References

  1. Aggarwal, D., Tamir, D., Last, M., Kandel, A.: A comparative study of artificial neural network and info fuzzy network as automated oracles in software testing. IEEE Trans. Syst. Man Cybern. 42(5), (2012)

    Google Scholar 

  2. Anderson, C., Mayrhauser, A., Marz, R.: On the use of neural networks to guide software testing activities. In: International Test Conference IEEE (1995)

    Google Scholar 

  3. Beizer, B.: Software Testing Techniques (2nd Ed.). Van Nostrand Reinhold Co., New York, NY, USA (1990)

    Google Scholar 

  4. Bertolino, A.: A Brief Essay on Software Testing, vol. 1. Wiley-IEEE (2005)

    Google Scholar 

  5. Bhasin, H.: Artificial life and cellular automata based automated test case generator. ACM SIGSOFT Softw. Eng. Notes 39(1), 1–5 (2014)

    Google Scholar 

  6. Bhasin, H.: Regression testing using genetic algorithms. (IJCSIT) Int. J. Comput. Sci. Inf. Technol. (2012)

    Google Scholar 

  7. Bhasin, H., Khanna, E.: Neural network based black box testing. ACM SIGSOFT Softw. Eng. Notes 39(4), 1–6 (2014)

    Google Scholar 

  8. Bhasin, H., Singla, N.: Cellaur-genetic test data generation. ACM SIGSOFT Softw. Eng. Notes 38(5), (2013)

    Google Scholar 

  9. Bhasin, H., Gupta, S., Kathuria, M.: Implementation of regression testing using fuzzy logic. Int. J. Appl. Innovation Eng. Manage. (2003)

    Google Scholar 

  10. Bhasin, H., Shewani, Goyal, D.: Test data generation using artificial life. Int. J. Comput. Appl. 67(12), (2013)

    Google Scholar 

  11. Bhasin, H., Singla, N., Sharma, S.: Cellular automata based test data generation. ACM SIGSOFT Softw. Eng. Notes 38(4), 1–6 (2013)

    Google Scholar 

  12. Jorgensen, P.C.: Software Testing A Craftman’s Approach. CRC Press (1995)

    Google Scholar 

  13. Kaner, C., Falk, J., Nguyen, H.Q.: Testing Computer Software. Wiley (1999)

    Google Scholar 

  14. Khan, E., Khan, F.: A comparative study of white box, black box and grey box testing techniques. Int. J. Adv. Comput. Sci. Appl. 3(6), (2012)

    Google Scholar 

  15. Kitchenham, B., et al.: Systematic literature reviews in software engineering-A systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (January 2009)

    Google Scholar 

  16. Larry, V., Kirkland, R., Glenn, W.: Using Neural Network for Functional Testing. IEEE (1995)

    Google Scholar 

  17. Myers, G.: The Art of Software Testing. Wiley (2004)

    Google Scholar 

  18. Paul, A.L., Byrne, P.: An Efficient Learning Algorithm for the Back Propagation Artificial Neural Network. IEEE (1990)

    Google Scholar 

  19. Saraph, P., Last, M., Kandel, A.: Test set generation and reduction with artificial neural networks. In: Last, D.M., Kandel, A., Bunke, H. (eds.) Artificial Intelligence Methods in Software Testing. World Scientific (2004)

    Google Scholar 

  20. Vanamali, M., Last, M., Kandel, A.: Using neural network in the software testing process. Int. J. Intell. Syst. 17(1), (2002)

    Google Scholar 

  21. Wu, L., Liu, B., Jin, Y., Xie, X.: Using back-propagation neural networks for functional software testing. In Anti-counterfeiting, Security and Identification,ASID 2008. 2nd International Conference (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Jamia Hamdard, New Delhi, India

    Harsh Bhasin

  2. Department of Computer Science, AITM, Faridabad, India

    Esha Khanna

  3. Department of Computer Science, Delhi Technological University, New Delhi, India

    Kapil Sharma

Authors
  1. Harsh Bhasin
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  2. Esha Khanna
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  3. Kapil Sharma
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Corresponding author

Correspondence to Harsh Bhasin .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

  2. Department of CSE, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Electronics and Communication, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

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Bhasin, H., Khanna, E., Sharma, K. (2016). Neural Network-Based Automated Priority Assigner. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_20

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  • DOI: https://doi.org/10.1007/978-81-322-2526-3_20

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2525-6

  • Online ISBN: 978-81-322-2526-3

  • eBook Packages: EngineeringEngineering (R0)

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