A Model to Measure Software Testing Effort Estimation in the Integrated Environment of ERNN, BMO & PSO

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-8
Year of Publication : 2021
Authors : Bijendra Singh, Dr. Ankit Kumar, Dheeraj Kumar Sahni, Divya Shree, Anu, Khushboo, Kapil Sirohi, Dhiraj Khurana
  10.14445/22315381/IJETT-V69I8P210

MLA 

MLA Style: Bijendra Singh, Dr. Ankit Kumar, Dheeraj Kumar Sahni, Divya Shree, Anu, Khushboo, Kapil Sirohi, Dhiraj Khurana  "A Model to Measure Software Testing Effort Estimation in the Integrated Environment of ERNN, BMO & PSO" International Journal of Engineering Trends and Technology 69.8(2021):81-88. 

APA Style: Bijendra Singh, Dr. Ankit Kumar, Dheeraj Kumar Sahni, Divya Shree, Anu, Khushboo, Kapil Sirohi, Dhiraj Khurana. A Model to Measure Software Testing Effort Estimation in the Integrated Environment of ERNN, BMO & PSO  International Journal of Engineering Trends and Technology, 69(8),81-88.

Abstract
The Elman neural network is a recurrent neural network which, is playing a significant role in effort estimation during software testing. It is reliable, much efficient in optimizing the results using several inputs from hidden layers while training the network. On the other hand, Barnacles Mating Optimizer (BMO) is a well-known optimization mechanism that could be used to optimize the result produced by Elman neural network during effort estimation. The particle swam optimization is considered a computational method that is capable to optimizes the problem by trying to enhance the solution with respect to the specified measure of quality iteratively. In this research, Elman recurrent neural network (ERNN), Barnacles Mating Optimizer (BMO), and particle swam optimization (PSO) are integrated to propose a multi-objective model to test the application. The PSO is applied to get more reliable and optimized results considering Accuracy, Precision, F-Score, and recall value. The research concludes that the proposed work has shown improvement in reliability as compared to the existing neural network models.

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Keywords
BMO, ERNN, Integrated Environment, Optimization, PSO.