Test case prioritization using modified genetic algorithm and ant colony optimization for regression testing

Regression testing (RT) plays an essential role in software maintenance. The occurrence of any new fault during the re-testing or modification process needs to be analyzed effectually. RT needs enormous effort to produce a higher fault detection rate. Test case prioritization (TCP) is an efficient w...

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Published in:International Journal of Advanced Technology and Engineering Exploration Vol. 9; no. 88; p. 384
Main Authors: Akila, T K, Malathi, A
Format: Journal Article
Language:English
Published: Bhopal Accent Social and Welfare Society 01.03.2022
Subjects:
Ant colony optimization
Experimentation
Fault detection
Genetic algorithms
Heuristic methods
Optimization
ISSN:2394-5443, 2394-7454
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Abstract Regression testing (RT) plays an essential role in software maintenance. The occurrence of any new fault during the re-testing or modification process needs to be analyzed effectually. RT needs enormous effort to produce a higher fault detection rate. Test case prioritization (TCP) is an efficient way to predict the fault detection rate. Various researchers modelled the TCP method to provide a single objective solution; however, this work concentrates on providing a multi-objective solution using a meta-heuristic optimization approach. Here, two different approaches known as ant colony optimization (ACO) and genetic algorithm (GA) are adopted to offer a multi-objective solution with a better fault detection rate. The characteristics of the ACO and GA are analyzed to prioritize the test case by combining the multi-dimensional characteristics under the test environment to enhance the fault detection rate. Here, some experimentation is made to compute the performance of the proposed model by evaluating the number of test cases, number of iterations, and ant traversal path. The proposed model shows better trade-off in contrast to the prevailing approaches where the fault detection rate of multi-objective (ACO and GA) model provides outcomes of 94.5%, 94.8%, 93.8%, 92.5%, 95.8%, 97.8%, 99.2%, and 93.5% respectively.
AbstractList Regression testing (RT) plays an essential role in software maintenance. The occurrence of any new fault during the re-testing or modification process needs to be analyzed effectually. RT needs enormous effort to produce a higher fault detection rate. Test case prioritization (TCP) is an efficient way to predict the fault detection rate. Various researchers modelled the TCP method to provide a single objective solution; however, this work concentrates on providing a multi-objective solution using a meta-heuristic optimization approach. Here, two different approaches known as ant colony optimization (ACO) and genetic algorithm (GA) are adopted to offer a multi-objective solution with a better fault detection rate. The characteristics of the ACO and GA are analyzed to prioritize the test case by combining the multi-dimensional characteristics under the test environment to enhance the fault detection rate. Here, some experimentation is made to compute the performance of the proposed model by evaluating the number of test cases, number of iterations, and ant traversal path. The proposed model shows better trade-off in contrast to the prevailing approaches where the fault detection rate of multi-objective (ACO and GA) model provides outcomes of 94.5%, 94.8%, 93.8%, 92.5%, 95.8%, 97.8%, 99.2%, and 93.5% respectively.
Author Akila, T K
Malathi, A
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SubjectTerms Ant colony optimization
Experimentation
Fault detection
Genetic algorithms
Heuristic methods
Optimization
Title Test case prioritization using modified genetic algorithm and ant colony optimization for regression testing
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