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

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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.

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Acknowledgements

The authors are very grateful to the editor and reviewers to provide the valuable insight and remarks, which aided in the advancement of the manuscript.

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  1. Department of IT, ABV-Indian Institute of Information Technology and management Gwalior, Gwalior, India

    Rohit Singh & Santosh Singh Rathore

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  1. Rohit Singh
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Singh, R., Rathore, S.S. Linear and non-linear bayesian regression methods for software fault prediction. Int J Syst Assur Eng Manag 13, 1864–1884 (2022). https://doi.org/10.1007/s13198-021-01582-1

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