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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Veröffentlicht in:Proceedings - International Symposium on Software Reliability Engineering S. 66 - 73
Hauptverfasser: Khoshgoftaar, T.M., Gao, K., Szabo, R.M.
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2001
Schlagworte:
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-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:SourceType-Scholarly Journals-2
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ObjectType-Conference Paper-1
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SourceType-Conference Papers & Proceedings-1
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ISBN:0769513069
9780769513065
ISSN:1071-9458
DOI:10.1109/ISSRE.2001.989459