Innovative Approaches for Machine-Driven Software Testing Using Neural Networks

Authors

  • 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

Keywords:

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

Abstract

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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Published

25.12.2023

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 https://ijisae.org/index.php/IJISAE/article/view/4315

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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