Standard Article

Software Quality Modeling as a Reliability Tool

  1. Naeem Seliya1,
  2. Taghi M. Khoshgoftaar2

Published Online: 15 SEP 2008

DOI: 10.1002/9780470050118.ecse393

Wiley Encyclopedia of Computer Science and Engineering

Wiley Encyclopedia of Computer Science and Engineering

How to Cite

Seliya, N. and Khoshgoftaar, T. M. 2008. Software Quality Modeling as a Reliability Tool. Wiley Encyclopedia of Computer Science and Engineering. 1–8.

Author Information

  1. 1

    University of Michigan—Dearborn, Dearborn, Michigan

  2. 2

    Florida Atlantic University, Boca Raton, Florida

Publication History

  1. Published Online: 15 SEP 2008


Software quality and reliability activities are important parts of software project development, and advances in related tools aim to deliver a high-quality and dependable software product. This article focuses on an effective tool in software quality engineering, namely software quality estimation models. Among existing software quality classification models, tree-based models are particularly attractive because of their model simplicity and ease in model interpretation. We perform an empirical comparative study of five tree-based software quality models. Software measurement and defect data of a very large real-world software system is used in our case study. The tree-based models are compared for model complexity, model structure, and classification accuracy. In addition, statistical analysis using ANOVA and multiple-pairwise hypothesis testing is used to evaluate the respective models based on their expected costs of misclassifica-tion. It is shown that at lower cost ratios the Sprint-Sliq model performs the best, whereas at higher cost ratios the CART model performs the best. Considering ease in model interpretation and model complexity, CART and S-PLUS are good choices. For a more robust model with good classification accuracy, Sprint-Sliq is a better tree-based model.


  • software quality;
  • classification trees;
  • software metrics;
  • software reliability;
  • statistical analysis