Genetic Algorithm Fitness Function Formulation for Test Data Generation with Maximum Statement Coverage

Avdeenko, Tatiana; Serdyukov, Konstantin

doi:10.1007/978-3-030-78743-1_34

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Modification Of the Fitness Function Based On Dynamic Change...1

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2024

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Cited by 5 publications

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“…The studies carried out in the articles [16][17][18] showed a relatively strong influence of the value of k on the coverage of the SUT. At k = 0, the coverage was minimal, reaching its maximum value at k = 10, after which it began to decline.…”

Section: Modification Of the Fitness Function Based On Dynamic Change...mentioning

confidence: 99%

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Section: Modification Of the Fitness Function Based On Dynamic Change...mentioning

confidence: 99%

Comparative Analysis of Feature Extraction Methods for Intelligence Estimation Based on Resting State EEG Data

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Genetic Algorithm Fitness Function Formulation for Test Data Generation with Maximum Statement Coverage

Cited by 5 publications

References 13 publications

Comparative Analysis of Feature Extraction Methods for Intelligence Estimation Based on Resting State EEG Data

Comparative Analysis of Feature Extraction Methods for Intelligence Estimation Based on Resting State EEG Data

Implementation of the Test Data Generation Algorithm Based on the Ant Colony Optimization Pheromone Model

Applicability of genetic algorithm in biochar combustion kinetics with double distributed activation energy model

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