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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| creator | Khoshgoftaar, T.M. Gao, K. Szabo, R.M. |
| description | 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. |
| doi_str_mv | 10.1109/ISSRE.2001.989459 |
| format | Conference Proceeding |
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| language | eng |
| recordid | cdi_ieee_primary_989459 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Computer science Economic forecasting Fault diagnosis Predictive models Software engineering Software quality Software reliability Software systems Software testing |
| title | An application of zero-inflated Poisson regression for software fault prediction |
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