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Adaptive Random Testing by Bisection and Comprehensive Distance

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Structured Object-Oriented Formal Language and Method (SOFL+MSVL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12028))

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Abstract

Adaptive random testing (ART) has been proved to be effective in improving the failure detection ability of random testing. As a lightweight ART algorithm, the ART by bisection (ART-B) can realize test case generation in a linear order of time complexity, but its ability for finding failures is not so strong. In this study, the dynamic bisection is used to generate candidates randomly from the empty regions as much as possible. For each candidate, two types of distances are taken into account. Then, a comprehensive distance metric is defined to determine the next test case from the candidate set. For the nearest neighbor query in the proposed ART by Bisection and Comprehensive Distance (ART-BCD), the distance-ware forgetting is adopted to ensure its computational cost is still in the linear order. To validate the effectiveness of ART-BCD algorithm, both simulation experiments and empirical studies are performed for comparative analysis. The experimental results show that ART-BCD is better than or comparable to the ART-B and other typical algorithms, such as the fixed-size-candidate-set ART (FSCS-ART), in most cases, especially for the block failure pattern.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61762040) and the Natural Science Foundation of the Jiangxi Province (Grant Nos. 20162BCB23036 and 20171ACB21031).

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Authors and Affiliations

  1. School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Chengying Mao, Mengting Quan & Zhilei Chen

  2. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC, 3122, Australia

    Tsong Yueh Chen

Authors
  1. Chengying Mao
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  2. Mengting Quan
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  3. Zhilei Chen
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  4. Tsong Yueh Chen
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Corresponding author

Correspondence to Chengying Mao .

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Editors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Huaikou Miao

  2. Institute of Computing Theory and Technology, Xidian University, Xi'an, China

    Cong Tian

  3. Hosei University, Tokyo, Japan

    Shaoying Liu

  4. Xidian University, Xi'an, Shaanxi, China

    Zhenhua Duan

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Mao, C., Quan, M., Chen, Z., Chen, T.Y. (2020). Adaptive Random Testing by Bisection and Comprehensive Distance. In: Miao, H., Tian, C., Liu, S., Duan, Z. (eds) Structured Object-Oriented Formal Language and Method. SOFL+MSVL 2019. Lecture Notes in Computer Science(), vol 12028. Springer, Cham. https://doi.org/10.1007/978-3-030-41418-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-41418-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41417-7

  • Online ISBN: 978-3-030-41418-4

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