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A Hybridized Artificial Neural Network for Automated Software Test Oracle

K. Kamaraj1,*, B. Lanitha2, S. Karthic3, P. N. Senthil Prakash4, R. Mahaveerakannan5

1 KPR Institute of Engineering and Technology, Coimbatore, 641048, India
2 Karpagam Academy of Higher Education, Coimbatore, 641021, India
3 Hindusthan College of Engineering and Technology, Coimbatore, 641032, India
4 RMK College of Engineering and Technology, Chennai, Tamil Nadu, India
5 Saveetha School of Engineering, Saveetha University, Chennai, 602105, India

* Corresponding Author: K. Kamaraj. Email:

Computer Systems Science and Engineering 2023, 45(2), 1837-1850.


Software testing is the methodology of analyzing the nature of software to test if it works as anticipated so as to boost its reliability and quality. These two characteristics are very critical in the software applications of present times. When testers want to perform scenario evaluations, test oracles are generally employed in the third phase. Upon test case execution and test outcome generation, it is essential to validate the results so as to establish the software behavior’s correctness. By choosing a feasible technique for the test case optimization and prioritization as along with an appropriate assessment of the application, leads to a reduction in the fault detection work with minimal loss of information and would also greatly reduce the cost for clearing up. A hybrid Particle Swarm Optimization (PSO) with Stochastic Diffusion Search (PSO-SDS) based Neural Network, and a hybrid Harmony Search with Stochastic Diffusion Search (HS-SDS) based Neural Network has been proposed in this work. Further to evaluate the performance, it is compared with PSO- SDS based artificial Neural Network (PSO-SDS ANN) and Artificial Neural Network (ANN). The Misclassification of correction output (MCO) of HS-SDS Neural Network is 6.37 for 5 iterations and is well suited for automated testing.


Cite This Article

K. Kamaraj, B. Lanitha, S. Karthic, P. N. S. Prakash and R. Mahaveerakannan, "A hybridized artificial neural network for automated software test oracle," Computer Systems Science and Engineering, vol. 45, no.2, pp. 1837–1850, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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