Neural-network techniques for software-quality evaluation

Author

Kumar, Renu ; Rai, Suresh ; Trahan, Jerry L.

Author_Institution

Dept. of Electr. & Comput. Eng., Louisiana State Univ., Baton Rouge, LA, USA

fYear

1998

fDate

19-22 Jan 1998

Firstpage

155

Lastpage

161

Abstract

Software quality modeling involves identifying fault-prone modules and predicting the number of errors in the early stages of the software development life cycle. This paper investigates the viability of several neural network techniques for software quality evaluation (SQE). We have implemented a principal component analysis technique (used in SQE) with two different neural network training rules, and have classified software modules as fault-prone or nonfault-prone using software complexity metric data. Our results reveal that neural network techniques provide a good management tool in a software engineering environment

Keywords

error analysis; neural nets; software development management; software metrics; software quality; unsupervised learning; backpropagation; errors prediction; fault-prone module identification; fault-prone modules; management; neural network techniques; neural network training rules; neural-network techniques; nonfault-prone modules; principal component analysis technique; software complexity metric data; software development life cycle; software engineering environment; software modules classification; software quality modeling; software-quality evaluation; Engineering management; Environmental management; Fault diagnosis; Management training; Neural networks; Predictive models; Principal component analysis; Programming; Software engineering; Software quality;

fLanguage

English

Publisher

ieee

Conference_Titel

Reliability and Maintainability Symposium, 1998. Proceedings., Annual

Conference_Location

Anaheim, CA

ISSN

0149-144X

Print_ISBN

0-7803-4362-X

Type

conf

DOI

10.1109/RAMS.1998.653706

Filename

653706