Operationalizing software reuse as a problem in inductive learning
Biggerstaff and Richter suggest that there are four fundamental subtasks associated with operationalizing the reuse process : finding reusable components, understanding these components, modifying these components, and composing components. Each of these subproblems can be re-expressed as a knowledge acquisition problem relative to producing a new representation able to facilitate the reuse process. In the current implementation of the Partial Metrics (PM), the focus is on operationalizing the first two subtasks.
This paper describes how the PM System performs the extraction of reusable procedural knowledge. An explanation of how PM works is carried out thorough the paper using as example the PASCAL system written by Goldberg  to implement the Holland's Genetic Algorithm.
Keywordssoftware reuse inductive learning decision trees chunking software metrics
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