A Neural Network Based Strategy (NNBS) for Automated Construction of Test Cases for Testing an Embedded System using Combinatorial Techniques

  • Authors

    • Lakshmi Prasad Mudarakola
    • J. K. R. Sastry
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.3.9271
  • Automated Test Case Generation, Input Domain Testing, Output Domain, Embedded Systems, Combinatorial Testing, Neural Networks

  • Abstract

    Testing an embedded system is required to locate bugs in software, diminish risk, development, repairs costs and to improve performance for both users and the company. Embedded software testing tools are useful for catching defects during unit, integration and system testing.   Embedded systems in many cases must be optimized by engaging crucial areas of the embedded systems considering all factors of the input domain.  The most important concern is to build a place of test cases depend on design of the requirements that can recognize more number of faults at a least rate and point in time in the major sections of an embedded system. This paper proposes a Neural Network Based strategy (NNBS) to generate optimized test cases based on the considerations of the system. A tool called NNTCG (Neural Network Test Case Generator) has been build up based on the method proposed in this paper. Test cases are generated for testing an embedded system using NNTCG and the same are used to determine the expected output through the neural network and the output generated from the actual firmware. The faulty paths within the firmware are determined when the output generated by the neural network is not same as the output generated by the firmware.

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  • How to Cite

    Mudarakola, L. P., & Sastry, J. K. R. (2017). A Neural Network Based Strategy (NNBS) for Automated Construction of Test Cases for Testing an Embedded System using Combinatorial Techniques. International Journal of Engineering & Technology, 7(1.3), 74-81. https://doi.org/10.14419/ijet.v7i1.3.9271

    Received date: 2018-01-24

    Accepted date: 2018-01-24

    Published date: 2017-12-31