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Hybrid K-means with neural network based Binary Cuckoo Search technique: a classifier for fault prediction in acceptance testing

Hybrid K-means with neural network based Binary Cuckoo Search technique: a classifier for fault... We propose a meta heuristic method using Binary Cuckoo Search to classify the generated test cases that helps to improve the test suite quality. In our proposed method, test cases for acceptance testing of our case study Hospital Management System are generated automatically through the existing tool, Code Pro, and then clustered by using K-means clustering algorithm. Then, the clustered test cases are classified according to their fault detection capability. We propose a novel classifier, hybrid K-means with neural network based Binary Cuckoo Search technique, for classification of generated test cases into two classes, faulty and faultless. The classified result is experimentally evaluated against the existing software metrics, average percentage of faults detected (APFD), problem tracking reports (PTR), and time and memory usage. From the experimental results, we observe that the average percentage of fault detected in our approach is higher than the existing method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Services Operations and Informatics Inderscience Publishers

Hybrid K-means with neural network based Binary Cuckoo Search technique: a classifier for fault prediction in acceptance testing

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1741-539X
eISSN
1741-5403
DOI
10.1504/IJSOI.2018.097493
Publisher site
See Article on Publisher Site

Abstract

We propose a meta heuristic method using Binary Cuckoo Search to classify the generated test cases that helps to improve the test suite quality. In our proposed method, test cases for acceptance testing of our case study Hospital Management System are generated automatically through the existing tool, Code Pro, and then clustered by using K-means clustering algorithm. Then, the clustered test cases are classified according to their fault detection capability. We propose a novel classifier, hybrid K-means with neural network based Binary Cuckoo Search technique, for classification of generated test cases into two classes, faulty and faultless. The classified result is experimentally evaluated against the existing software metrics, average percentage of faults detected (APFD), problem tracking reports (PTR), and time and memory usage. From the experimental results, we observe that the average percentage of fault detected in our approach is higher than the existing method.

Journal

International Journal of Services Operations and Informatics – Inderscience Publishers

Published: Jan 1, 2018

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