Test Data Generation Algorithm of Combinatorial Testing Based on Differential Evolution

Wang, Jianfeng; Wei, Chang'an; Jiang, Shouda

doi:10.1109/imccc.2013.123

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…After all test data in individuals execute the program under test, each individual's fitness is calculated according to (10). Then, selection, crossover and mutation operations will be executed until one of the terminal conditions is obtained.…”

Section: ) Calculation Of the Fitness Of Individualsmentioning

confidence: 99%

“…Search-based technology is used to search for data automatically. For example, Particle Swarm Optimization (PSO) [7,8], Difference Evolutionary algorithm (DE) [9][10][11], Artificial Bee Colony algorithm (ABC) [12,13], Firework Algorithm (FA) [14] are applied by scholars. They convert test data generation problems into combinatorial optimization problems and evolve to generate test data that meet the requirements.…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary Generation Method of Test Data for Multiple Paths Based on Coverage Balance

et al. 2021

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Test data generation is one of the main tasks of software testing. The goal of test data generation based on search algorithms is to automate the task and find test data that meet test criteria. In this study, an evolutionary generation method for test data that cover multiple paths is proposed. Firstly, the method obtains the coverage balance for each target path based on the number of individuals traversing the true and false branches of branch nodes, and calculates the individual's influence on coverage balance before and after an individual joining based on our previous work. Then, according to the number of branch nodes on each target path, the weights of different target paths are designed to obtain the individual fitness to adjust the evolution process and quickly generate test data covering multiple target paths. Finally, the proposed method is compared with existing techniques. Experimental results of benchmark programs and industrial use cases show that the proposed method can effectively improve the efficiency of test data generation for multiple paths.

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Section: ) Calculation Of the Fitness Of Individualsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evolutionary Generation Method of Test Data for Multiple Paths Based on Coverage Balance

et al. 2021

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SFLA‐based heuristic method to generate software structural test data

Ghaemi

Arasteh

2019

J Software Evolu Process

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Software testing is one of the significant stages in software development life cycle which is a costly and time‐consuming task. Automatic tests data generation is one of the traditional techniques to reduce the cost and time spent in software testing. Different evolutionary algorithms have been proposed to generate test data which cover target paths in a software program. In this paper, shuffled frog leaping algorithm (SFLA) is proposed to generate structural test data. The proposed SFLA algorithm is characterized by high convergence speed and simple implementation. In the proposed SFLA, branch coverage is used as the fitness function to generate effective test data. For comparing the performance of the proposed SFLA with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC), seven benchmark programs were used. The results indicated that the proposed SFLA has an average of 99.99% for branch coverage, average 99.97% for success rate, and 2.03 for the average number of generation for covering all branches.

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Test Data Generation Algorithm of Combinatorial Testing Based on Differential Evolution

Cited by 2 publications

References 9 publications

An Evolutionary Generation Method of Test Data for Multiple Paths Based on Coverage Balance

An Evolutionary Generation Method of Test Data for Multiple Paths Based on Coverage Balance

SFLA‐based heuristic method to generate software structural test data

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