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Software fault localization using BP neural network based on function and branch coverage

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Abstract

Software failure is inevitable with the increase in scale and complexity of the software. Existing fault localization techniques based on neural networks take statement coverage information and test case execution results into account to train the network. In this paper, we propose an effective approach for fault localization based on back-propagation neural network which utilizes branch and function coverage information along with test case execution results to train the network. We investigated our approach using Siemens suite. Our experimental result shows that our proposed approach performs on average 23.50–44.27% better than existing fault localization techniques.

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Notes

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Authors and Affiliations

  1. DOS Lab, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Abha Maru & Durga Prasad Mohapatra

  2. GRL, Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Arpita Dutta

  3. RS Lab, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    K. Vinod Kumar

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  1. Abha Maru
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  2. Arpita Dutta
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  3. K. Vinod Kumar
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  4. Durga Prasad Mohapatra
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Correspondence to Arpita Dutta.

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Maru, A., Dutta, A., Kumar, K.V. et al. Software fault localization using BP neural network based on function and branch coverage. Evol. Intel. 14, 87–104 (2021). https://doi.org/10.1007/s12065-019-00318-2

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