Construction and testing of polynomials predicting software maintainability

Abstract

Assessing software maintainability is complicated by the many aspects of software and software systems that affect maintenance activity. In an earlier study of 35 published works on software maintainability, we coalesced software maintainability definitions into a hierarchical structure containing 92 known maintainability attributes (Oman et al., 1991). The tree structure is refined through successive subtrees until a leaf node, representing an identified maintainability attribute, is defined. Our hierarchy serves as a taxonomic definition for software maintainability that is compatible with the 35 published works on which it is based. In a subsequent study, we showed how these software maintainability attributes can be measured via a battery of metrics and discussed methods to create a single value (hybrid metric) that represents the maintainability of the software system (Oman and Hagemeister, 1992). We were charged with identifying the smallest set of metrics useful in predicting the maintainability of a suite of software modules.’ In this article, we show how the set of applicable measures can be reduced to the minimum number of metrics useful in predicting maintainability. The maintainability assessment polynomials presented here are accurate models of the test data on which they were constructed. We do not claim that these are the only models for predicting maintainability, nor do we claim they are the best overall, but they are automated working models that can be used to quickly and easily predict software maintainability. The models presented here have been used in conjunction with two prototype software tools developed at the University of Idaho: Hewlett-Packard Software Maintainability Estimation Spreadsheet