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

Abstract

Abstract-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.

Identifying influential metrics in the combined metrics approach of fault prediction

“…Regarding the predictive capability of inheritance metrics i.e. DIT and number of children (NOC), contradictory results have been reported in literature ( Okutan and Yildiz 2012 ; Yu 2012 ; Basili et al 1996 ; Gyimothy et al 2005 ; Subramanyam and Krishnan 2003 ). Nevertheless, our results indicate that their combination (interaction) with other metrics like WMC, LCOM and RFC becomes a determining factor in the accuracy of the fault prediction model.…”

“…Fault prediction models based on different modelling techniques have been widely used to improve software quality for the last three decades. Out of the many modelling techniques used by researchers, regression and its variants are still drawing a major portion of the attention of research communities (Basili et al 1996 ; Denaro et al 2003 ; Yu 2012 ; Bibi et al 2008 ; Thwin and Quah 2005 ; Briand et al 2000 ; Khoshgoftaar et al 2002 ; Gyimothy et al 2005 ). Comparison of regression with other evolutionary algorithm based techniques has also been appraised as well (Raj Kiran and Ravi 2008 ; Radjenovic et al 2013 ).…”

Towards an ensemble based system for predicting the number of software faults

“…Similar types of studies were reported in Ostrand, Weyuker, and Bell (2004) and Bell, Ostrand, and Weyuker (2006) . Some other studies using negative binomial regression (NBR) technique have been reported by Janes et al (2006) and Yu (2012) . In Janes et al (2006) a NBR model was constructed for a telecommunication system using several object-oriented metrics for the prediction of fault counts.…”

“…The results found that NBR produced better results for the prediction of fault counts. Yu (2012) evaluated the capability of negative binomial regression technique and then compared its performance with logistic regression technique for fault prediction. The results indicated that for the prediction of fault-prone software modules logistic regression yielded better performance compared to NBR model.…”

Multi-project Regression based Approach for Software Defect Number Prediction

“…To the best of our knowledge, previous studies mainly focused on classifying software modules as defect-prone or not. Only a few studies built and evaluated models for defect number prediction [5][6][7]. These studies built models independently for each project, which may ignore the relatedness among multiple projects.…”