Biggerstaff and Richter suggest that there are four fundamental subtasks associated with operationalizing the reuse process [1]. They are finding reusable components, understanding these components, modifying these components, and composing components. Each of these sub-problems can be re-expressed as a knowledge acquisition sub-problem relative to producing a new representation for the components that make them more suitable for future reuse.
In this paper, we express the first two subtasks for the software reuse activity, as described by Biggerstaff and Richter, as a problem in Machine Learning. From this perspective, the goal of software reuse is to learn to recognize reusable software in terms of code structure, run-time behavior, and functional specification. The Partial Metrics (PM) System supports the acquisition of reusable software at three different levels of granularity: the system level, the procedural level, and the code segment level. Here, we describe how the system extracts procedural knowledge from an example Pascal software system that satisfies a set of structural, behavioral, and functional constraints. These constraints are extracted from a set of positive and negative examples using inductive learning techniques. The constraints are expressed quantitatively in terms of various quality models and metrics. The general characteristics of learned constraints that were extracted from a variety of applications libraries are discussed.
