Title
Modeling the search landscape of metaheuristic software clustering algorithms
Abstract
Software clustering techniques are useful for extracting architectural information about a system directly from its source code structure. This paper starts by examining the Bunch clustering system, which uses metaheuristic search techniques to perform clustering. Bunch produces a subsystem decomposition by partitioning a graph formed from the entities (e.g., modules) and relations (e.g., function calls) in the source code, and then uses a fitness function to evaluate the quality of the graph partition. Finding the best graph partition has been shown to be a NP-hard problem, thus Bunch attempts to find a sub-optimal result that is "good enough" using search algorithms. Since the validation of software clustering results often is overlooked, we propose an evaluation technique based on the search landscape of the graph being clustered. By gaining insight into the search landscape, we can determine the quality of a typical clustering result. This paper defines how the search landscape is modeled and how it can be used for evaluation. A case study that examines a number of open source systems is presented.
Year
DOI
Venue
2003
10.1007/3-540-45110-2_153
GECCO
Keywords
Field
DocType
open source system,metaheuristic software,bunch clustering system,graph partition,search algorithm,metaheuristic search technique,source code,best graph partition,source code structure,typical clustering result,search landscape,np hard problem,search space,fitness function,graph partitioning
Fuzzy clustering,Data mining,Search algorithm,Computer science,Theoretical computer science,Artificial intelligence,Bidirectional search,Cluster analysis,Graph partition,Mathematical optimization,Correlation clustering,Beam search,Machine learning,Best-first search
Conference
Volume
ISSN
ISBN
2724
0302-9743
3-540-40603-4
Citations 
PageRank 
References 
8
0.67
15
Authors
2
Name
Order
Citations
PageRank
Brian S. Mitchell155722.86
Spiros Mancoridis288856.82