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Comparison of Search based Techniques for Automated Test Data Generation

by Ruchika Malhotra, Chand Anand, Nikita Jain, Apoorva Mittal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 23
Year of Publication: 2014
Authors: Ruchika Malhotra, Chand Anand, Nikita Jain, Apoorva Mittal
10.5120/16732-6881

Ruchika Malhotra, Chand Anand, Nikita Jain, Apoorva Mittal . Comparison of Search based Techniques for Automated Test Data Generation. International Journal of Computer Applications. 95, 23 ( June 2014), 4-8. DOI=10.5120/16732-6881

@article{ 10.5120/16732-6881,
author = { Ruchika Malhotra, Chand Anand, Nikita Jain, Apoorva Mittal },
title = { Comparison of Search based Techniques for Automated Test Data Generation },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 23 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number23/16732-6881/ },
doi = { 10.5120/16732-6881 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:11.342182+05:30
%A Ruchika Malhotra
%A Chand Anand
%A Nikita Jain
%A Apoorva Mittal
%T Comparison of Search based Techniques for Automated Test Data Generation
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 23
%P 4-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the essential parts of the software development process is software testing as it ensures the delivery of a good quality and reliable software. Various techniques and algorithms have been developed to carry out the testing process. This paper deals with utility of the nature based algorithms namely Genetic Algorithm, Ant Colony Optimization algorithm and Artificial Bee Colony algorithm in automatic generation of optimized test suite for a given set of programs. The performance of algorithms is evaluated using various factors such as number of paths covered, number of iterations, number of test cases produced and time taken for generation of test suite. The results of performance analysis concluded that Artificial Bee Colony algorithm is more efficient as compared to other mentioned algorithms and can be employed for optimized test suite generation for various complex programs or software.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Artificial bee colony (ABC) Ant colony optimization (ACO) Genetic algorithm (GA) Software testing Automatic Test Suite Generation.

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