A Graph Pattern Matching Approach to Software Architecture Recovery
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
A user-assisted approach to component clustering
Journal of Software Maintenance: Research and Practice
Extraction and Visualization of Architectural Structure Based on Cross References among Object Files
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Applications of clustering techniques to software partitioning, recovery and restructuring
Journal of Systems and Software - Special issue: Applications of statistics in software engineering
Spectral and meta-heuristic algorithms for software clustering
Journal of Systems and Software - Special issue: Software reverse engineering
Using software evolution to focus architectural recovery
Automated Software Engineering
Revisiting the ΔIC approach to component recovery
Science of Computer Programming - Software analysis, evolution and re-engineering
API-Based and Information-Theoretic Metrics for Measuring the Quality of Software Modularization
IEEE Transactions on Software Engineering
A desiderata for refactoring-based software modularity improvement
Proceedings of the 3rd India software engineering conference
Identifying Extract Class refactoring opportunities using structural and semantic cohesion measures
Journal of Systems and Software
Journal of Software Maintenance and Evolution: Research and Practice
Identification and application of Extract Class refactorings in object-oriented systems
Journal of Systems and Software
Efficient software clustering technique using an adaptive and preventive dendrogram cutting approach
Information and Software Technology
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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In this paper, we present a supervised clustering framework for recovering the architecture of a software system. The technique measures the association between the system components (such as files) in terms of data and control flow dependencies among the groups of highly related entities that are scattered throughout the components. The application of data mining techniques allows to extract the maximum association among the groups of entities. This association is used as a measure of closeness among the system files in order to collect them into subsystems using an optimization clustering technique. A two-phase supervised clustering process is applied to incrementally generate the clusters and control the quality of the system decomposition.In order to address the complexity issues, the whole clustering space is decomposed into sub-spaces based on the association property. At each iteration, the sub-spaces are analyzed to determine the most eligible sub-space for the next cluster, which is then followed by an optimization search to generate a new cluster.