Revisiting the ΔIC approach to component recovery
Science of Computer Programming - Software analysis, evolution and re-engineering
Program restructuring using clustering techniques
Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
Automated clustering to support the reflexion method
Information and Software Technology
Hierarchical Clustering for Software Architecture Recovery
IEEE Transactions on Software Engineering
Software Engineering
Extending the reflexion method for consolidating software variants into product lines
Software Quality Control
Combating architectural degeneration: a survey
Information and Software Technology
Journal of Software Maintenance and Evolution: Research and Practice
Information and Software Technology
Controlling software architecture erosion: A survey
Journal of Systems and Software
Clustering methodologies for software engineering
Advances in Software Engineering
Leveraging design rules to improve software architecture recovery
Proceedings of the 9th international ACM Sigsoft conference on Quality of software architectures
Cooperative clustering for software modularization
Journal of Systems and Software
Clustering Software Components for Component Reuse and Program Restructuring
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
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Software systems need to evolve as businessrequirements, technology and environment change. Assoftware is modified to accommodate the requiredchanges, its structure deteriorates. There is increaseddeviation from the actual design and architecture. Veryoften, documentation is not updated to reflect thesechanges thus making it more and more difficult tounderstand, manage and maintain these systems.Researchers have applied various techniques to recoverthe components and architecture of such softwaresystems. The use of clustering techniques has recentlybeen explored for reverse engineering and softwarearchitecture recovery. There is a need to tailorclustering algorithms and similarity measures to caterto software. In this paper, we present a new algorithmfor finding inter-cluster distance. We compare theperformance of some popular similarity measures forthis algorithm using two test systems and suggestvariations of the similarity measures which show betterresults for software clustering.