Traxtor: An Automatic Software Test Suit Generation Method Inspired by Imperialist Competitive Optimization Algorithms

Arasteh, Bahman; Hosseini, Seyed Mohamad Javad

doi:10.1007/s10836-022-05999-9

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…The complexity of implementation and maintenance ICA algorithm [15] Higher coverage and speed Low stability Scenario-based Method [16] Higher scenario coverage Mainly for design testing, not for source-code testing Hyper Heuristic Model-based Testing [17] Higher program state coverage and lower execution time…”

Section: Methods Merits Demeritsmentioning

confidence: 99%

“…Arasteh et al [15] offered an automated approach for generating structural test data was proposed. The program source code is statically evaluated in the first phase, and the structural information necessary for the subsequent stages is extracted.…”

Section: Related Workmentioning

confidence: 99%

“…The collection of benchmark programs has been employed in several earlier studies to assess the effectiveness of test data [9][10][11][12][13][14][15]. The characteristics of the benchmark programs employed in this investigation are listed in Table 4.…”

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confidence: 99%

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A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

Arasteh,

Kiani

et al. 2024

Mathematics

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The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.

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Section: Methods Merits Demeritsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%