Computing similarity in a reuse library system: an AI-based approach

This paper presents an AI based library system for software reuse, called AIRS, that allows a developer to browse a software library in search of components that best meet some stated requirement. A component is described by a set of (feature, term) pairs. A feature represents a classification criterion, and is defined by a set of related terms. The system allows to represent packages (logical units that group a set of components) which are also described in terms of features. Candidate reuse components and packages are selected from the library based on the degree of similarity between their descriptions and a given target description. Similarity is quantified by a nonnegative magnitude (distance) proportional to the effort required to obtain the target given a candidate. Distances are computed by comparator functions based on the subsumption, closeness, and package relations. We present a formalization of the concepts on which the AIRS system is based. The functionality of a prototype implementation of the AIRS system is illustrated by application to two different software libraries: a set of Ada packages for data structure manipulation, and a set of C components for use in Command, Control, and Information Systems. Finally, we discuss some of the ideas we are currently exploring to automate the construction of AIRS classification libraries.