Innovative Approaches for Machine-Driven Software Testing Using Neural Networks


  • Amit Bhanushali Independent Researcher, Quality Assurance Manager, West Virginia University, WV, USA
  • Karan Gupta Senior Data Scientist, SunPower Corporation, TX, USA
  • Manvendra Sharma Embedded Software Development Enineer, Amazon, USA


Neural Network, Software Testing, Test Oracle, Synthetic Dataset, Software Quality Optimization


The primary objective of software testing is to create fresh sets of test cases that accurately represent software defects. Once these test cases are created, Test Oracle gives a procedure for how the program should respond to each test. Reducing the time and effort spent on finding and fixing bugs, while maintaining as much data as possible, is possible via careful evaluation of the application and the selection of an effective method for optimizing and prioritizing test cases. The suggested neural network outperforms ANN in terms of accurate output misclassification, at least on the synthetic dataset.


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How to Cite

Bhanushali, A. ., Gupta, K. ., & Sharma, M. . (2023). Innovative Approaches for Machine-Driven Software Testing Using Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 724–732. Retrieved from



Research Article