Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant

Liguo Yu
2012 International Journal of Information Technology and Computer Science  
Negative binomial regression has been proposed as an approach to predicting fault-prone software modules. However, little work has been reported to study the strength, weakness, and applicability of this method. In this paper, we present a deep study to investigate the effectiveness of using negative binomial regression to predict fault-prone software modules under two different conditions, selfassessment and forward assessment. The performance of negative binomial regression model is also
more » ... model is also compared with another popular fault prediction model-binary logistic regression method. The study is performed on six versions of an open-source objected-oriented project, Apache Ant. The study shows (1) the performance of forward assessment is better than or at least as same as the performance of self-assessment; (2) in predicting fault-prone modules, negative binomial regression model could not outperform binary logistic regression model; and (3) negative binomial regression is effective in predicting multiple errors in one module.
doi:10.5815/ijitcs.2012.08.08 fatcat:w2uo7cnvijaqtopudr6cs62nmm