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An Optimized Adaptive Random Partition Software Testing by Using Bacterial Foraging Algorithm

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Computational Vision and Bio Inspired Computing

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 28))

Abstract

Software testing is a procedure of investigating a software product to find errors. Optimized Adaptive Random Partition Testing (OARPT) is a combined approach which comprises of Adaptive Testing (AT) and Random Partition Testing (RPT) in an alternative manner depending on test case. ARPT consists of two strategies are ARPT 1 and ARPT 2. The parameters of ARPT 1 and ARPT 2 need to be assessed for different software with different number of test cases and programs. The process of assessing the parameters of ARPT consumes high computational overhead and therefore it is more necessary to optimize parameters of ARPT. In this paper, the parameters of ARPT 1 and ARPT 2 are optimized by using Bacterial Foraging Algorithm (BFA) which improves the performance of ARPT software testing strategies. The experiments are conducted in different software and the proposed method improves defect detection efficiency and high code coverage, reduces time consumption and reduces memory utilization.

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Author information

Authors and Affiliations

  1. Department of BCA & M.Sc. (SS), Sri Krishna Arts and Science College, Bharathiar University, Coimbatore, 641008, Tamilnadu, India

    K. Devika Rani Dhivya

  2. Department of Computer Science, Chikkanna Government Arts College, Bharathiar University, Tirupur, 641602, Tamilnadu, India

    V. S. Meenakshi

Authors
  1. K. Devika Rani Dhivya
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  2. V. S. Meenakshi
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Corresponding author

Correspondence to K. Devika Rani Dhivya .

Editor information

Editors and Affiliations

  1. Karunya University , Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

  2. RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

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Devika Rani Dhivya, K., Meenakshi, V.S. (2018). An Optimized Adaptive Random Partition Software Testing by Using Bacterial Foraging Algorithm. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_47

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  • DOI: https://doi.org/10.1007/978-3-319-71767-8_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71766-1

  • Online ISBN: 978-3-319-71767-8

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