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Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

U. Geetha1,*, Sharmila Sankar2
1 Department of Information Technology, B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600048, India
2 Department of Computer Science and Engineering, B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600048, India
* Corresponding Author: U. Geetha. Email:

Computer Systems Science and Engineering 2022, 42(3), 899-917. https://doi.org/10.32604/csse.2022.022621

Received 13 August 2021; Accepted 24 September 2021; Issue published 08 February 2022

Abstract

Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.

Keywords

Regression testing; test suite reduction; test case generation

Cite This Article

U. Geetha and S. Sankar, "Multi-objective modified particle swarm optimization for test suite reduction (mompso)," Computer Systems Science and Engineering, vol. 42, no.3, pp. 899–917, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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