Automatic Test Data Generation for Software Path Testing Using Evolutionary Algorithms

Latiu, Gentiana Ioana; Cret, Octavian; Văcariu, Lucia

doi:10.1109/eidwt.2012.25

Cited by 24 publications

(12 citation statements)

References 15 publications

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“…It is used to generate automatic test data for data flow coverage with using dominance concept between two nodes, which is compared to both GA and PSO for generation of automatic test cases to demonstrate its superiority. Ioana Latiu et al [11] presented a comparison between three important evolutionary algorithms used for automatic test data generation, The results obtained based on the proposed approaches suggest that evolutionary testing strategies were very well suited to generate test data which cover a target path inside a software program.…”

Section: Related Workmentioning

confidence: 95%

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Automatic test data generation based on reduced adaptive particle swarm optimization algorithm

et al. 2015

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Section: Related Workmentioning

confidence: 95%

“…If the branch function actual value is true, then the corresponding branch function values of each branch are shown as formula (11).…”

Section: Fitness Function Constructionmentioning

confidence: 99%

Automatic test data generation based on reduced adaptive particle swarm optimization algorithm

et al. 2015

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“…A typical value is defined as 1.001 [24]. The obtained distance [25,26] is shown in Table 1. Table 1.…”

Section: Fitness Functionmentioning

confidence: 99%

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020

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Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

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“…It begins with a group of randomly generated individuals called as initial population. The best solution can be found by a number of particles constituting a swarm, moving around in a particular real valued N-dimensional search space and adjusting their flying according to own and other's flying experience [37]. A fitness is defined to evaluate each particle from the Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  Evolutionary algorithms for path coverage test data generation and optimization… (Deepti Bala Mishra) 507 population.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Evolutionary algorithms for path coverage test data generation and optimization: a review

Mishra

Acharya

Mishra

2019

IJEECS

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Software testing is very time consuming, labor-intensive and complex process. It is found that 50% of the resources of the software development are consumed for testing. Testing can be done in two different ways such as manual testing and automatic testing. Automatic testing can overcomes the limitations of manual testing by decreasing the cost and time of testing process. Path testing is the strongest coverage criteria among all white box testing techniques as it can detect about 65% of defects present in a SUT. With the help of path testing, the test cases are created and executed for all possible paths which results in 100% statement coverage and 100% branch coverage .This paper presents a systematic review of test data generation and optimization for path testing using Evolutionary Algorithms (EAs). Different EAs like GA, PSO, ACO, and ABCO based methods has been already proposed for automatic test case generation and optimization to achieve maximum path coverage.

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Automatic Test Data Generation for Software Path Testing Using Evolutionary Algorithms

Cited by 24 publications

References 15 publications

Automatic test data generation based on reduced adaptive particle swarm optimization algorithm

Automatic test data generation based on reduced adaptive particle swarm optimization algorithm

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

Evolutionary algorithms for path coverage test data generation and optimization: a review

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