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 software quality. Two forms of neural computation are considered: fuzzy neural computation based on fuzzy sets and rough neural computation based on rough sets. Both forms of neural computation are defined in the context of the McCall software quality evaluation framework, which is hierarchical. This hierarchy has three levels: factors (highest-level based user views of software quality), criteria (mid-level based on characteristics of software), and metrics (lowest level based on quantification of software quality). The introduction of a neural approach to estimating the number of changes needed to achieve software quality according to a project requirement is motivated by the need to harness the complexities inherent in the relationships between factors, criteria and metrics. The architecture of both types of networks is given. The results of calibrating both types of networks are also given. The performance of the two forms of neural computation is compared.