Paper Info
Title
A Measure of the Modularisation of Sequential Software Versions Using Random Graph Theory.
Abstract
Software module clustering is the problem of automatically partitioning the structure of a software system using low-level dependencies in the source code to understand and improve the system's architecture. Munch, a clustering tool based on search-based software engineering techniques, was used to modularise a unique dataset of sequential source code software versions. This paper investigates whether the dataset used for the modularisation resembles a random graph by computing the probabilities of observing certain connectivity. Modularisation will not be possible with data that resembles random graphs. Thus, this paper demonstrates that our real world time-series dataset does not resemble a random graph except for small sections where there were large maintenance activities. Furthermore, the random graph metric can be used as a tool to indicate areas of interest in the dataset, without the need to run the modularisation.
Year
DOI
Venue
2014
10.1007/978-3-319-14358-3_10
AGILE METHODS: LARGE-SCALE DEVELOPMENT, REFACTORING, TESTING, AND ESTIMATION
Keywords
Field
DocType
software module clustering,modularisation,SBSE,random graph,time-series,fitness function
Architecture,Software modules,Random graph,Pattern recognition,Source code,Computer science,Theoretical computer science,Software system,Fitness function,Artificial intelligence,Cluster analysis,Software versioning
Conference
Volume
ISSN
Citations 
199
1865-1348
0
PageRank 
References 
Authors
0.34
13
4
Name
Order
Citations
PageRank
Mahir Arzoky1105.22
Stephen Swift242731.32
Steve Counsell31732117.90
James Cain431.09