System Structure Analysis: Clustering with Data Bindings
IEEE Transactions on Software Engineering - Annals of discrete mathematics, 24
A measure for composite module cohesion
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A reverse engineering environment based on spatial and visual software interconnection models
SDE 5 Proceedings of the fifth ACM SIGSOFT symposium on Software development environments
Using Neural Networks to Modularize Software
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Software reflexion models: bridging the gap between source and high-level models
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Assessing modular structure of legacy code based on mathematical concept analysis
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Extracting concepts from file names: a new file clustering criterion
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Identifying objects using cluster and concept analysis
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Concept Analysis for Module Restructuring
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A Metric-Based Approach to Detect Abstract Data Types and State Encapsulations
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Extracting and Restructuring the Design of Large Systems
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A significant aspect in applying the Reflexion Method is the mapping of components found in the source code onto the conceptual components defined in the hypothesized architecture. To date, this mapping is established manually, which requires a lot of work for large software systems. In this paper, we present a new approach, in which clustering techniques are applied to support the user in the mapping activity. The result is a semi-automated mapping technique that accommodates the automatic clustering of the source model with the user's hypothesized knowledge about the system's architecture. This paper describes three case studies in which the semi-automated mapping technique, called HuGMe, has been applied successfully to extend a partial map of real-world software applications. In addition, the results of another case study from an earlier publication are summarized, which lead to comparable results. We evaluated the extended versions of two automatic software clustering techniques, namely, MQAttract and CountAttract, with oracle mappings. We closely study the influence of the degree of completeness of the existing mapping and other controlling variables of the technique to make reliable suggestions. Both clustering techniques were able to achieve a mapping quality where more than 90% of the automatic mapping decisions turned out to be correct. Moreover, the experiments indicate that the attraction function (CountAttract') based on local coupling and cohesion is more suitable for semi-automated mapping than the approach MQAttract' based on a global assessment of coupling and cohesion.