Automated classification and retrieval of reusable software components

• Published in: Journal of the American Society for Information Science and Technology 2008-02, Vol.59 (4), p.613-627
• Main Authors: Yao, Haining, Etzkorn, Letha H., Virani, Shamsnaz
• Format: Article
• Language: English
• Subjects: Automatic classification; Automation; Classification; Computer Software; Exact sciences and technology; Information and communication sciences; Information retrieval; Information retrieval systems. Information and document management system; Information science. Documentation; Interfaces. Software; Internet; Natural language; Queries; Representations; Retrieval; Reusable components; Sciences and techniques of general use; Search strategies; Searching; Semantics; Semiotics; Software; Software reuse; Statistical analysis; Studies; Systems development; World Wide Web
• ISSN: 1532-2882; 2330-1635; 1532-2890; 2330-1643
• DOI: 10.1002/asi.20775

The authors describe their research which 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 (WWW). Using automation and smart (semantic) techniques, their approach speeds up the search and retrieval of reusable software components, while retaining good accuracy, and therefore improves the affordability of software reuse. A program understanding of software components and natural language understanding of user queries was employed. Then the software component descriptions were compared by matching the resulting semantic representations of the user queries to the semantic representations of the software components to search for software components that best match the user queries. A proof of concept system was developed to test the authors' 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.