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

Rohit Singh () and Santosh Singh Rathore ()
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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

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 4, No 25, 1864-1884

Abstract: 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.

Keywords: Machine learning techniques; Bayesian learning; Regression; Covariance matrix; Monte carlo markov chain (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01582-1

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