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Reseach Article

Enhanced GUI Test Case Generation Method using Two-stage Classification Method

by E. Vijayakumar, M. Punithavalli
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 13
Year of Publication: 2013
Authors: E. Vijayakumar, M. Punithavalli
10.5120/10528-5511

E. Vijayakumar, M. Punithavalli . Enhanced GUI Test Case Generation Method using Two-stage Classification Method. International Journal of Computer Applications. 63, 13 ( February 2013), 29-33. DOI=10.5120/10528-5511

@article{ 10.5120/10528-5511,
author = { E. Vijayakumar, M. Punithavalli },
title = { Enhanced GUI Test Case Generation Method using Two-stage Classification Method },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 13 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number13/10528-5511/ },
doi = { 10.5120/10528-5511 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:16.500616+05:30
%A E. Vijayakumar
%A M. Punithavalli
%T Enhanced GUI Test Case Generation Method using Two-stage Classification Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 13
%P 29-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software testing is a task of quality assurance where the main aim is to identify errors. Graphical User Interfaces (GUIs), a class of Event-Driven Software (EDS), is increasingly used to increase the human-to-computer interaction. General tests are not applied directly to GUIs because of the increased number of states generated because of huge number of permutations of input events. This paper proposes techniques that use a reduction-based test case generation model that is enhanced by identifying feasible and infeasible test sequences. The proposed method uses a two-stage classification process, where two classifiers, BPNN (Back Propagation Neural Network) and Support Vector Machine (SVM), are used. The main goal here is to improve the performance of the second classifier, SVM, by using the results of the first classifier, BPNN. Experimental results show that the proposed method has increased the accuracy of classification.

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Index Terms

Computer Science
Information Sciences

Keywords

Graphical User Interface Test Case Generation Infeasible Test Sequences Support Vector Machine Back Propagation Neural Network