DIV-TBAT algorithm for test suite reduction in software testing

Researchers have investigated different approaches to maintain the minimum cost and effort in regression testing. Here, test suite reduction is a common technique to decrease the cost of regression testing by removing the redundant test cases from the test suite and then, obtaining a representative...

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Bibliographic Details
Published inIET software Vol. 12; no. 3; pp. 271 - 279
Main Authors Rushikesh Sugave, Shounak, Patil, Suhas Haribhau, Eswara Reddy, B
Format Journal Article
LanguageEnglish
Published The Institution of Engineering and Technology 01.06.2018
Subjects
ATAP measure
Boolean functions
Boolean logic
DIV-TBAT algorithm
genetic algorithms
greedy algorithms
greedy search algorithm
mathematics computing
program testing
reduced test suite
redundant test cases
regression testing
Research Article
search problems
software maintenance
software testing
test suite reduction
ATAP measure
program testing
greedy algorithms
software testing
mathematics computing
regression testing
genetic algorithms
software maintenance
test suite reduction
greedy search algorithm
Boolean functions
Boolean logic
DIV-TBAT algorithm
reduced test suite
redundant test cases
search problems
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Summary:Researchers have investigated different approaches to maintain the minimum cost and effort in regression testing. Here, test suite reduction is a common technique to decrease the cost of regression testing by removing the redundant test cases from the test suite and then, obtaining a representative set of test cases that still yield a high level of code coverage. Accordingly, here, the authors have developed two various techniques for test suite reduction. In the first technique, ATAP measure is newly developed to find the reduced test suite with the help of greedy search algorithm. In the second technique, DIV-TBAT (DIVersity-based BAT) algorithm is newly devised based on the mechanisms of Boolean logic within BAT algorithm which improve diversity during the search process. The proposed techniques are experimented using eight programs from SIR subject programs and the performance study is conducted using nine different evaluation metrics based on different research questions. The comparative analysis is performed with the existing algorithms like GreedyRatio, GreedyEIrreplaceability, diversity-based genetic algorithm, TBAT, and TAP, to prove the performance improvement over the eight software programs considered.
ISSN:1751-8806
1751-8814
1751-8814
DOI:10.1049/iet-sen.2017.0130