SODOS: a software documentation support environment—its definition
IEEE Transactions on Software Engineering
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Object-oriented software engineering
Object-oriented software engineering
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Information retrieval
ConceptBase—a deductive object base for meta data management
Journal of Intelligent Information Systems - Special issue: deductive and object-oriented databases
Communications of the ACM
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Information Retrieval
Design-code traceability for object-oriented systems
Annals of Software Engineering
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
ACM Transactions on Software Engineering and Methodology (TOSEM)
PRO-ART: Enabling Requirements Pre-Traceability
ICRE '96 Proceedings of the 2nd International Conference on Requirements Engineering (ICRE '96)
Conceptual clustering in information retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatically identifying changes that impact code-to-design traceability during evolution
Software Quality Control
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Capturing the traceability relationship between software requirement and design allows developers to check whether the design meets the requirement and to analyze the impact of requirement changes on the design. This paper presents an approach for identifying the classes in object-oriented software design that realizes a given use case, which leverages ideas and technologies from Information Retrieval (IR) and Text Clustering area. First, we represent the use case and all classes as vectors in a vector space constructed with the keywords coming from them. Then, the classes are clustered based on their semantic relevance and the cluster most related to the use case is identified. Finally, we supplement the raw cluster by analyzing structural relationships among classes. We conduct an experiment by using this clustering-based approach to a system - Resource Management Software. We calculate and compare the precision and recall of our approach and nonclustering approaches, and get promising results.