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Linear and non-linear bayesian regression methods for software fault prediction

Author

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  • Rohit Singh

    (ABV-Indian Institute of Information Technology and management Gwalior)

  • Santosh Singh Rathore

    (ABV-Indian Institute of Information Technology and management Gwalior)

Abstract

Faults are most likely to occur during the coding phase of software development. If, before the testing process, we can predict parts of code that are more prone to faults, then a large amount of time, software cost could be saved, and the software’s overall quality could be improved. Various researchers have previously attempted to predict software faults using numerous machine learning techniques in order to identify whether software modules are fault-prone or not. Ranking the software modules based on their fault content has rarely been explored before. Additionally, Bayesian methods have not been explored before for this task. We aim to investigate both linear and non-linear Bayesian regression methods for software fault prediction in this work. We develop and evaluate fault prediction models for two scenarios: intra-release prediction and cross-release prediction. The experimental investigation is conducted on 46 different software project versions. We use mean absolute error, and root means square error, and fault percentage average as performance measures. The results showed that Bayesian NLR outperformed linear regression and other used machine learning approaches or produced at least comparable performance. Bayesian linear regression method performed moderately.

Suggested Citation

  • Rohit Singh & Santosh Singh Rathore, 2022. "Linear and non-linear bayesian regression methods for software fault prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1864-1884, August.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01582-1
    DOI: 10.1007/s13198-021-01582-1
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    References listed on IDEAS

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    1. Lizhen Lin & David B. Dunson, 2014. "Bayesian monotone regression using Gaussian process projection," Biometrika, Biometrika Trust, vol. 101(2), pages 303-317.
    2. Tammy Harris & James W. Hardin, 2013. "Exact Wilcoxon signed-rank and Wilcoxon Mann–Whitney ranksum tests," Stata Journal, StataCorp LP, vol. 13(2), pages 337-343, June.
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    Cited by:

    1. Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 549-568, March.

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