Radial basis function neural network based approach to test oracle-Reference-Cited by-同舟云学术

Radial basis function neural network based approach to test oracle

Published:2011-09-30 Issue:5 Volume:36 Page:1-5
ISSN:0163-5948
Container-title:ACM SIGSOFT Software Engineering Notes
language:en
Short-container-title:SIGSOFT Softw. Eng. Notes

Author:

Sangwan Om Prakash¹,Bhatia Pradeep Kumar²,Singh Yogesh³

Affiliation:

1. Gautam Buddha University, Greater Noida, Uttar Pradesh, India

2. GJU of Science & Technology, Hisar, Haryana, India

3. M.S. University of Baroda, Gujarat, India

Abstract

Software testing is an important discipline, and consumes significant amount of effort. A proper strategy is required to design and generate test cases systematically and effectively. In this paper automated software test case generation with Radial Basis Function Neural Network (RBFNN) has been proposed and empirically validated with the help of a case study and compared with other techniques of soft computing. Experimental results show that RBFNN is one of the best technique for automated test case generation.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/2020976.2020992

Reference30 articles.

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3. A neural net based approach to Test Oracle

4. Empirical Studies on Applications of Neural Networks as Approximation Oracle

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