Title

Predictive Modeling Techniques Of Software Quality From Software Measures

Authors

Authors

T. M. Khoshgoftaar; J. C. Munson; B. B. Bhattacharya;G. D. Richardson

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Softw. Eng.

Keywords

Average Relative Error; Model Predictive Quality; Model Quality Of Fit; Program Changes; Regression Analysis; Software Complexity Metrics; Regression; Metrics; Computer Science, Software Engineering; Engineering, Electrical &; Electronic

Abstract

The objective in the construction of models of software quality is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of the quality of an evolving software system. Measures of software quality and software complexity to be used in this modeling process exhibit systematic departures of the normality assumptions of regression modeling. This paper introduces two new estimation procedures and compares their performance in the modeling of software quality from software complexity in terms of the predictive quality and the quality of fit with the more traditional least squares and least absolute value estimation techniques. The two new estimation techniques did produce regression models with better quality of fit and predictive quality when applied to data obtained from two actual software development projects.

Journal Title

Ieee Transactions on Software Engineering

Volume

18

Issue/Number

11

Publication Date

1-1-1992

Document Type

Article

Language

English

First Page

979

Last Page

987

WOS Identifier

WOS:A1992KB70400006

ISSN

0098-5589

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