This dissertation describes our research that improves software reuse by using an automated approach to semantically search for and retrieve reusable software components in large software component repositories and on the World Wide Web. Using automation and smart (semantic) techniques, our approach speeds up the search and retrieval of reusable software components while retaining good accuracy, and therefore improves the affordability of software reuse.;In the past fifteen years, software reuse has been a worthwhile goal to reduce software costs and improve software quality as well as programmer productivity. Among other topics, software reuse research examines strategies for setting up libraries containing reusable code components, as well as strategies for classification and retrieval techniques to help software engineers select the component from the software library that is the most appropriate for their purposes.;We employed program understanding of software components and natural language understanding of user queries. We then compared the software component descriptions matching the resulting semantic representations of the user queries to the semantic representations of the software components in order to search for software components that best match the user queries. Research was performed on semantic representations, including conversion from one semantic representation format to another and matching of semantic representations. In our approach, software components are described semantically in conceptual graphs (CGs) or the Resource Description Framework (RDF). Semantic conversion research in this dissertation addresses CG to RDF conversion.;A proof of concept system was developed to test our approach. The results of this proof of concept system were compared to human experts, and statistical analysis was performed on the collected experimental data. The results from these experiments demonstrate that this automated semantic-based approach for software reusable component classification and retrieval is successful when compared to the labor-intensive results from the experts, thus showing that this approach can significantly benefit software reuse classification and retrieval.
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