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A LambdaMart-Based High-Accuracy Approach for Software Automatic Fault Localization

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Wireless Algorithms, Systems, and Applications (WASA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12938))

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Abstract

Software debugging or fault localization is a very significant task in software development and maintenance, which directly determines the quality of software. Traditional methods of fault localization rely on manual investigation, which takes too much time in large-scale software development. To mitigate this problem, many automatic fault localization techniques have been proposed which can effectively lighten the burden of programmers. However, the quality of these techniques is not enough to meet the practical requirements. In order to improve the accuracy of fault localization, we propose LBFL, a LambdaMart-based high-accuracy approach for software automatic fault localization, which can integrate software’s diversified features and achieve very high accuracy. To realize that, LBFL first extracts the static and dynamic features and normalizes them. Then these features are gathered on LambdaMart algorithm for training. Finally, LBFL sorts the code statements according to the model and generates a list which can help developers to locate faults. Exhaustive experiments indicate that LBFL can locate 76 faults in Top-1, which has at least 217% improvements over nine single techniques and has 55% improvements over ABFL approach on the Defects4J dataset.

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Acknowledgment

This work is supported in part by the National Natural Science Foundation of China (62004077, 61972219), the RD Program of Shenzhen (JCYJ20190813174403598, SGDX20190918101201696), the National Key Research and Development Program of China (2018YFB1800601), the Overseas Research Cooperation Fund of Tsinghua Shenzhen International Graduate School (HW2021013).

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Authors and Affiliations

  1. College of Informatics, Huazhong Agricultural University, Wuhan, China

    Yunhao Xiao & Fang Tian

  2. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Xi Xiao

  3. School of Computer Science, Shenzhen Institute of Information Technology, Shenzhen, China

    Guangwu Hu

  4. Peng Cheng Laboratory, Shenzhen, China

    Xi Xiao

Authors
  1. Yunhao Xiao
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  2. Xi Xiao
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  3. Fang Tian
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  4. Guangwu Hu
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Corresponding author

Correspondence to Guangwu Hu .

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Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhe Liu

  2. Shanghai Jiao Tong University, Shanghai, China

    Fan Wu

  3. Missouri University of Science and Technology, Rolla, MO, USA

    Sajal K. Das

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Xiao, Y., Xiao, X., Tian, F., Hu, G. (2021). A LambdaMart-Based High-Accuracy Approach for Software Automatic Fault Localization. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12938. Springer, Cham. https://doi.org/10.1007/978-3-030-86130-8_20

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  • DOI: https://doi.org/10.1007/978-3-030-86130-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86129-2

  • Online ISBN: 978-3-030-86130-8

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