ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Spectral and meta-heuristic algorithms for software clustering
Journal of Systems and Software - Special issue: Software reverse 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
Software Engineering
Modeling the search landscape of metaheuristic software clustering algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Clustering methodologies for software engineering
Advances in Software Engineering
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Software clustering algorithms are used to create high-level views of a system's structure using source code-level artifacts. Software clustering is an active area of research that has produced many clustering algorithms. However, we have seen very little work that investigates how the results of these algorithms can be evaluated objectively in the absence of a benchmark decomposition, or without the active participation of the original designers of the system.Ideally, for a given system, an agreed upon reference (benchmark) decomposition of the system's structure would exist, allowing the results of various clustering algorithms to be compared against it. Since such benchmarks seldom exist, we seek alternative methods to gain confidence in the quality of results produced by software clustering algorithms.In this paper we present a tool that supports the evaluation of software clustering results in the absence of a benchmark decomposition.