A system for graph clustering based on user hints
VIP '00 Selected papers from the Pan-Sydney workshop on Visualisation - Volume 2
Towards a Simple Clustering Criterion Based on Minimum Length Encoding
ECML '02 Proceedings of the 13th European Conference on Machine Learning
EDBT '02 Proceedings of the Worshops XMLDM, MDDE, and YRWS on XML-Based Data Management and Multimedia Engineering-Revised Papers
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
RIPPLES: Tool for Change in Legacy Software
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Information-Theoretic Software Clustering
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
On the Automatic Modularization of Software Systems Using the Bunch Tool
IEEE Transactions on Software Engineering
Revisiting the ΔIC approach to component recovery
Science of Computer Programming - Software analysis, evolution and re-engineering
Clustering large software systems at multiple layers
Information and Software Technology
Hierarchical Clustering for Software Architecture Recovery
IEEE Transactions on Software Engineering
Software Engineering
Combating architectural degeneration: a survey
Information and Software Technology
Kadre: domain-specific architectural recovery for scientific software systems
Proceedings of the IEEE/ACM international conference on Automated software engineering
Journal of Software Maintenance and Evolution: Research and Practice
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
An empirical study on the developers' perception of software coupling
Proceedings of the 2013 International Conference on Software Engineering
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Experimental evaluation of clustering techniques for component recovery is necessary in order to analyze their strengths and weaknesses in comparison to other techniques. For comparable evaluations of automatic clustering techniques, a common reference corpus of freely available systems is needed for which the actual components are known. The reference corpus is used to measure recall and precision of automatic techniques. For this measurement, a standard scheme for comparing the components recovered by a clustering technique to components in the reference corpus is required. This paper describes both the process of setting up reference corpora and ways of measuring recall and precision of automatic clustering techniques.For methods with human intervention, controlled experiments should be conducted. This paper additionally proposes a controlled experiment as a standard for evaluating manual and semi-automatic component recovery methods that can be conducted cost-effectively.