Software Clustering - protected
https://wiki.eecs.yorku.ca/project/cluster/
2026-05-12T03:22:20+00:00Software Clustering
https://wiki.eecs.yorku.ca/project/cluster/
https://wiki.eecs.yorku.ca/project/cluster/_media/wiki:dokuwiki-128.pngtext/html2010-05-08T15:20:52+00:00Anonymous (anonymous@undisclosed.example.com)ACDC Algorithm
https://wiki.eecs.yorku.ca/project/cluster/protected:acdc?rev=1273332052&do=diff
ACDC Algorithm
History
Developed by Vassilios Tzerpos.
Algorithm Intent
ACDC produces flat or nested decompositions of a software system. The algorithm assigns meaningful names to the clusters it creates.
Factbase Properties
The factbase includes only dependencies betweentext/html2010-05-06T14:14:19+00:00Anonymous (anonymous@undisclosed.example.com)Agglomerative Algorithms
https://wiki.eecs.yorku.ca/project/cluster/protected:agglomerative?rev=1273155259&do=diff
Agglomerative Algorithms
History
Algorithm Intent
Factbase Properties
The factbase is a generic dependency data table where each row describes one entity to be clustered. Each column contains the value for a specific attribute.
Clustering Objectives
Process Descriptiontext/html2010-05-06T14:08:36+00:00Anonymous (anonymous@undisclosed.example.com)Bunch Algorithm
https://wiki.eecs.yorku.ca/project/cluster/protected:bunch?rev=1273154916&do=diff
Bunch Algorithm
History
Algorithm Intent
Bunch produces flat decompositions of a software system that exhibit high cohesion and low coupling.
Factbase Properties
The factbase is a module dependency graph (MDG).
Clustering Objectives
The Bunch algorithm constructs software decompositions with maximum cohesion and minimum coupling.text/html2010-05-06T14:12:48+00:00Anonymous (anonymous@undisclosed.example.com)LIMBO Algorithm
https://wiki.eecs.yorku.ca/project/cluster/protected:limbo?rev=1273155168&do=diff
LIMBO Algorithm
History
The algorithm was developed by Periklis Andritsos and Vassilios Tzerpos.
Algorithm Intent
To generate decompositions that exhibit the least information lost when entities are represented by their clusters.
Factbase Propertiestext/html2010-05-06T13:58:37+00:00Anonymous (anonymous@undisclosed.example.com)Decompositions
https://wiki.eecs.yorku.ca/project/cluster/protected:nested_flat_decomposition?rev=1273154317&do=diff
Decompositions
The output of a software clustering algorithm is either flat decompositions or nested decompositions of software systems.
Flat
A flat decomposition does not contain nested clusters. There is one level of clusters and one level of entities. An example of flat decomposition is shown below.text/html2009-05-10T15:54:45+00:00Anonymous (anonymous@undisclosed.example.com)Subsystem Patterns
https://wiki.eecs.yorku.ca/project/cluster/protected:subsystem_patterns?rev=1241970885&do=diff
Subsystem Patternstext/html2009-05-08T17:36:27+00:00Anonymous (anonymous@undisclosed.example.com)Template
https://wiki.eecs.yorku.ca/project/cluster/protected:template?rev=1241804187&do=diff
Template
History
Algorithm Intent
A description of the goals behind this
software clustering algorithm. Examples of such goals include:
* To decompose a software system into subsystems
* To recover the architecture of a software system
*text/html2010-05-06T13:57:35+00:00Anonymous (anonymous@undisclosed.example.com)Terminology
https://wiki.eecs.yorku.ca/project/cluster/protected:terms?rev=1273154255&do=diff
Terminology
Entity
An entity is defined as anything recognized as being capable of an independent existence and which can be uniquely identified.
In software clustering, an entity may be a class, a source file, a function call statement, or a software module.