A system for graph clustering based on user hints
VIP '00 Selected papers from the Pan-Sydney workshop on Visualisation - Volume 2
Clustering and concept analysis for software evolution
IWPSE '01 Proceedings of the 4th International Workshop on Principles of Software Evolution
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
A language-independent software renovation framework
Journal of Systems and Software - Special issue: Software reverse engineering
A modified genetic algorithm for software clustering problem
AIC'06 Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications
Software Engineering
Understanding existing software with use case map scenarios
SAM'02 Proceedings of the 3rd international conference on Telecommunications and beyond: the broader applicability of SDL and MSC
Clustering methodologies for software engineering
Advances in Software Engineering
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A crucial step in understanding a large legacy software system is to decompose it into meaningful subsystems, which can be separately studied. This decomposition can be done either manually or automatically by a software-clustering algorithm (SCA). Similar versions of a software system can be expected to have similar decompositions. We say an SCA is stable if small changes in its input (the software system) produce small changes in its output (the decomposition). This paper defines stability formally, explains why it is an essential property for an SCA, and gives experimental results from evaluating the stability of various decomposition algorithms suggested in the literature.