Calibration of software quality: Fuzzy neural and rough neural computing approaches

This paper compares different neural computing approaches to estimating the number of changes needed in a software product based on assessments of sof tware quality. Two forms of neural computation are considered: fuzzy neural computation based on fuzzy sets and rough neural computation based on roug h sets. Both forms of neural computation are defined in the context of the McCall software quality evaluation framework, which is hierarchical. This h ierarchy has three levels: factors (highest-level based user views of softw are quality), criteria (mid-level based on characteristics of software), an d metrics (lowest level based on quantification of software quality). The i ntroduction of a neural approach to estimating the number of changes needed to achieve software quality according to a project requirement is motivate d by the need to harness the complexities inherent in the relationships bet ween factors, criteria and metrics. The architecture of both types of netwo rks is given. The results of calibrating both types of networks are also gi ven. The performance of the two forms of neural computation is compared. (C ) 2001 Elsevier Science B.V. All rights reserved.