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Proposal of Iterative Genetic Algorithm for Test Suite Generation

Author

Listed:
  • Ankita Bansal

    (Netaji Subhas University of Technology, India)

  • Abha Jain

    (Delhi University, India)

  • Abhijeet Anand

    (Netaji Subhas Institute of Technology, India)

  • Swatantra Annk

    (Netaji Subhas Institute of Technology, India)

Abstract

Huge and reputed software industries are expected to deliver quality products. However, industry suffers from a loss of approximately $500 billion due to shoddy software quality. The quality of the product in terms of its accuracy, efficiency, and reliability can be revamped through testing by focusing attention on testing the product through effective test case generation and prioritization. The authors have proposed a test-case generation technique based on iterative listener genetic algorithm that generates test cases automatically. The proposed technique uses its adaptive nature and solves the issues like redundant test cases, inefficient test coverage percentage, high execution time, and increased computation complexity by maintaining the diversity of the population which will decrease the redundancy in test cases. The performance of the technique is compared with four existing test-case generation algorithms in terms of computational complexity, execution time, coverage, and it is observed that the proposed technique outperformed.

Suggested Citation

  • Ankita Bansal & Abha Jain & Abhijeet Anand & Swatantra Annk, 2021. "Proposal of Iterative Genetic Algorithm for Test Suite Generation," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 12(1), pages 111-130, January.
    • Handle: RePEc:igg:jismd0:v:12:y:2021:i:1:p:111-130
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