Clustering-Based Support for Software Architecture Restructuring
The maintenance of long-living software systems is an essential topic in today’s software engineering practice and research. Software Architecture Restructuring is an important task to adjust these systems to current requirements and to keep them maintainable. Niels Streekmann introduces an approach to Software Architecture Restructuring that semi-automates this task by introducing graph clustering. The approach provides an iterative process that systematically incorporates human architectural knowledge for the improvement of the restructuring result. Thus, it supports the task of planning the transfer of an existing system to a target architecture and aims at reducing the required manual effort.
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analysis Andreas Winter arbitrary decisions architec architectural erosion architecture reconstruction architecture restructuring automated Best Weights changes clustering result coarse-grained complete mapping computation configuration consolidating iterations context created decomposition criteria defined definition dependency type weights dependency weights described detailed design model edges elements to target evaluation evolution existing system Experiment Figure Furthermore goal graph clustering hierarchical clustering horseshoe model IEEE influence initial mapping input interfaces Koschke Legacy Systems mapped source elements mapping of source MARE approach MARE clustering algorithm MARE Method MDSD metamodel metrics migration Model-Driven modules MoJo MoJoFM NCMSE nodes Object Management Group object-oriented PCMSE reengineering reference mapping reflexion method restructuring projects reverse engineering Section service-oriented architectures similarity function Software Architecture Software Engineering Software Maintenance software systems source code source system model strategy Streekmann structure target architecture target components target system tecture thesis tion ture unwanted dependencies updating rule viewtype Weights set