Count Models for Software Quality Estimation

• Published in: IEEE transactions on reliability Vol. 56; no. 2; pp. 212 - 222
• Main Authors: Khoshgoftaar, T.M., Kehan Gao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Classification; Computer industry; Computer programs; Context modeling; Counting; Fault diagnosis; Faults; Linear regression; Logistic regression; Logistics; Mathematical models; Modules; Poisson regression; Predictive models; principal components analysis; Programming; Regression; Software; software fault prediction; software metrics; Software quality; software quality classification; Software systems; Studies; zero-inflated Poisson
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2007.896757

Identifying which software modules, during the software development process, are likely to be faulty is an effective technique for improving software quality. Such an approach allows a more focused software quality & reliability enhancement endeavor. The development team may also like to know the number of faults that are likely to exist in a given program module, i.e., a quantitative quality prediction. However, classification techniques such as the logistic regression model (lrm) cannot be used to predict the number of faults. In contrast, count models such as the Poisson regression model (prm), and the zero-inflated Poisson (zip) regression model can be used to obtain both a qualitative classification, and a quantitative prediction for software quality. In the case of the classification models, a classification rule based on our previously developed generalized classification rule is used. In the context of count models, this study is the first to propose a generalized classification rule. Case studies of two industrial software systems are examined, and for each we developed two count models, (prm, and zip), and a classification model (lrm). Evaluating the predictive capabilities of the models, we concluded that the prm, and the zip models have similar classification accuracies as the lrm. The count models are also used to predict the number of faults for the two case studies. The zip model yielded better fault prediction accuracy than the prm. As compared to other quantitative prediction models for software quality, such as multiple linear regression (mlr), the prm, and zip models have a unique property of yielding the probability that a given number of faults will occur in any module