Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant
Liguo Yu · International Journal of Information Technology and Computer Science · 2012
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 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.