Information-Theoretic Software Clustering
IEEE Transactions on Software Engineering
ACM SIGKDD Explorations Newsletter
Clustering large software systems at multiple layers
Information and Software Technology
An improved methodology on information distillation by mining program source code
Data & Knowledge Engineering
Hierarchical Clustering for Software Architecture Recovery
IEEE Transactions on Software Engineering
Using trace sampling techniques to identify dynamic clusters of classes
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
Proceedings of the 2nd India software engineering conference
Clustering for Monitoring Software Systems Maintainability Evolution
Electronic Notes in Theoretical Computer Science (ENTCS)
Software clustering based on behavioural features
SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
Automatic generation of abstract views for legacy software comprehension
Proceedings of the 3rd India software engineering conference
A desiderata for refactoring-based software modularity improvement
Proceedings of the 3rd India software engineering conference
A practice-driven systematic review of dependency analysis solutions
Empirical Software Engineering
Analysis and visualization of behavioral dependencies among distributed objects based on UML models
MoDELS'06 Proceedings of the 9th international conference on Model Driven Engineering Languages and Systems
Clustering methodologies for software engineering
Advances in Software Engineering
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The reverse engineering literature contains many software clustering approaches that attempt to cluster large software systems based on the static dependencies between software artifacts. However, the usefulness of clustering based on dynamic dependencies has not been investigated. It is possible that dynamic clusterings can provide a fresh outlook on the structure of a large software system. In this paper, we present an approach for the evaluation of dynamic clusterings. We apply this approach to a large open source software system, and present experimental results that suggest that dynamic clusterings have considerable merit.