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Genetic Algorithm Based on Test Suite Prioritization for Software Testing in Neural Network

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Proceedings of First International Conference on Mathematical Modeling and Computational Science

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1292))

Abstract

The paper addresses research in the area of software testing, which has a critical part of performing in software quality assurance. It becomes very inefficient for a tester to re-execute massive number of test cases again and again for small variations. The proposed artificial neural network with the genetic algorithm-based case suite prioritization (ANNGCSP) achieves better performance compared. New proposed algorithm combined the rational functional tester (RFT) tool, and new proposed algorithm combined open source tool in terms of APFD metric.

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Author information

Authors and Affiliations

  1. Department of Computer Application, Women’s Christian College, Chennai, India

    K. Hema Shankari

  2. Department of Computer Science, Ethiraj College for Women, Chennai, India

    S. Mathivilasini

  3. Department of Computer Application, DRBCCC Hindu College, Chennai, India

    Dilli Arasu

  4. Department of Information Technology, Vels Institute of Science, Technology and Advanced Studies, Chennai, India

    G. Suseendran

Authors
  1. K. Hema Shankari
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  2. S. Mathivilasini
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  3. Dilli Arasu
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  4. G. Suseendran
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Corresponding author

Correspondence to G. Suseendran .

Editor information

Editors and Affiliations

  1. Department of Creative Technologies and Product Design, National Taipei University of Business, Taoyuan, Taiwan

    Sheng-Lung Peng

  2. Department of Mathematics, Beijing Jiaotong University, Beijing, China

    Rong-Xia Hao

  3. Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishnanagar, West Bengal, India

    Souvik Pal

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Cite this paper

Hema Shankari, K., Mathivilasini, S., Arasu, D., Suseendran, G. (2021). Genetic Algorithm Based on Test Suite Prioritization for Software Testing in Neural Network. In: Peng, SL., Hao, RX., Pal, S. (eds) Proceedings of First International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing, vol 1292. Springer, Singapore. https://doi.org/10.1007/978-981-33-4389-4_37

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