Software defect prediction with zero-inflated Poisson models

TL;DR

This paper explores the use of zero-inflated Poisson models for software defect prediction, demonstrating their slight advantage over traditional models using AIC for model selection.

Contribution

It introduces the application of various zero-inflated Poisson models and compares their effectiveness in software defect prediction using multiple R packages.

Findings

Zero-inflated models outperform traditional models slightly

Bayesian and maximum likelihood approaches yield comparable results

AIC effectively guides model selection

Abstract

In this work we apply several Poisson and zero-inflated models for software defect prediction. We apply different functions from several R packages such as pscl, MASS, R2Jags and the recent glmmTMB. We test the functions using the Equinox dataset. The results show that Zero-inflated models, fitted with either maximum likelihood estimation or with Bayesian approach, are slightly better than other models, using the AIC as selection criterion.