Neural-network techniques for software-quality evaluation

Document Type

Conference Proceeding

Publication Date

1-1-1998

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 non-fault-prone using software complexity metric data. Our results reveal that neural network techniques provide a good management tool in a software engineering environment.

Publication Source (Journal or Book title)

Proceedings of the Annual Reliability and Maintainability Symposium

First Page

155

Last Page

161

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