An Effectiveness Measure for Software Clustering Algorithms

Authors:
Affiliations:
Venue:
IWPC '04 Proceedings of the 12th IEEE International Workshop on Program Comprehension
Year:
2004

Citing 0
Cited 10

Program restructuring using clustering techniques

Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
Hierarchical Clustering for Software Architecture Recovery

IEEE Transactions on Software Engineering
Reverse-engineering of an industrial software using the unified process: an experiment

SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
Visual comparison of software architectures

Proceedings of the 5th international symposium on Software visualization
A biting-down approach to hierarchical decomposition of object-oriented systems based on structure analysis

Journal of Software Maintenance and Evolution: Research and Practice
Clustering methodologies for software engineering

Advances in Software Engineering
Recovering design patterns to support program comprehension

Proceedings of the 2nd international workshop on Evidential assessment of software technologies
Putting the developer in-the-loop: an interactive GA for software re-modularization

SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
An empirical study on the developers' perception of software coupling

Proceedings of the 2013 International Conference on Software Engineering
Cooperative clustering for software modularization

Journal of Systems and Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

Selecting an appropriate software clustering algorithmthat can help the process of understanding a large softwaresystem is a challenging issue. The effectiveness of a particularalgorithm may be influenced by a number of differentfactors, such as the types of decompositions produced, orthe way clusters are named.In this paper, we introduce an effectiveness measure forsoftware clustering algorithms based on MoJo distance,and describe an algorithm that calculates its value. We alsopresent experiments that demonstrate its improved performanceover previous measures, and show how it can be usedto assess the effectiveness of software clustering algorithms.