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Reseach Article

Mutual Information Gain based Test Suite Reduction

by Meenu Dave, Rashmi Agrawal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 4
Year of Publication: 2017
Authors: Meenu Dave, Rashmi Agrawal
10.5120/ijca2017914358

Meenu Dave, Rashmi Agrawal . Mutual Information Gain based Test Suite Reduction. International Journal of Computer Applications. 168, 4 ( Jun 2017), 1-9. DOI=10.5120/ijca2017914358

@article{ 10.5120/ijca2017914358,
author = { Meenu Dave, Rashmi Agrawal },
title = { Mutual Information Gain based Test Suite Reduction },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 4 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number4/27860-2017914358/ },
doi = { 10.5120/ijca2017914358 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:12.360963+05:30
%A Meenu Dave
%A Rashmi Agrawal
%T Mutual Information Gain based Test Suite Reduction
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 4
%P 1-9
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The test suite optimization during test case generation can save time and cost. The paper presents an information theory based metric to filter the redundant test cases and reduce the test suite size while, maintaining the coverage of the requirements and with minimum loss to mutant coverage. The paper propose two versions, RR and RR2. RR filters test cases for each requirement, where as, RR2 filters till the target coverage is achieved. The paper suggests the time and phase for the implementation of the algorithms, based on results. The results show that the proposed algorithms are effective at optimizing the testing process by saving time and resource.

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Index Terms

Computer Science
Information Sciences

Keywords

Information Theory Optimization Mutual Information Gain Test suite size reduction test data generation