An application of zero-inflated Poisson regression for software fault prediction

Poisson regression model is widely used in software quality modeling. When the response variable of a data set includes a large number of zeros, Poisson regression model will underestimate the probability of zeros. A zero-inflated model changes the mean structure of the pure Poisson model. The predi...

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Bibliographic Details
Published in:Proceedings - International Symposium on Software Reliability Engineering pp. 66 - 73
Main Authors: Khoshgoftaar, T.M., Gao, K., Szabo, R.M.
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 2001
Subjects:
Application software
Computer science
Economic forecasting
Fault diagnosis
Predictive models
Software engineering
Software quality
Software reliability
Software systems
Software testing
ISBN:0769513069, 9780769513065
ISSN:1071-9458
Online Access:Get full text
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Summary:Poisson regression model is widely used in software quality modeling. When the response variable of a data set includes a large number of zeros, Poisson regression model will underestimate the probability of zeros. A zero-inflated model changes the mean structure of the pure Poisson model. The predictive quality is therefore improved. In this paper, we examine a full-scale industrial software system and develop two models, Poisson regression and zero-inflated Poisson regression. To our knowledge, this is the first study that introduces the zero-inflated Poisson regression model in software reliability. Comparing the predictive qualities of the two competing models, we conclude that for this system, the zero-inflated Poisson regression model is more appropriate in theory and practice.
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ISBN:0769513069
9780769513065
ISSN:1071-9458
DOI:10.1109/ISSRE.2001.989459