Software Clustering

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.
====== 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
     * To discover possible ways to restructure the software


Explicitly stating algorithm intent can simplify selecting a software
clustering algorithm for reverse engineers, as well as understanding a
clustering algorithm for software clustering researchers.

====== Factbase Properties ======
A description of the input that the software clustering algorithm expects (an algorithm's input is often called a factbase since it contains facts
extracted from the software system).  For example, software clustering
algorithms often expect that the factbase includes only dependencies
between modules.

====== Clustering Objectives ======
A description of the ideal output
of the software clustering algorithm. Based on experience or heuristics,
the designer of the algorithm decides what a meaningful decomposition of
a software system should look like. For example, a software decomposition with maximum cohesion and minimum coupling. 

====== Process Description ======
A brief description of the main idea
behind the algorithm's implementation. For example: "ACDC creates
clusters by detecting established subsystem patterns in the given
software system. Software entities that are not clustered this way are
assigned to the subsystem that depends the most on them".

====== Decomposition Properties ======
A description of the properties
of the decompositions that the software clustering algorithm creates.
These properties are either a direct result of the clustering objective
(e.g. Bunch creates decompositions whose value of the objective function
is higher than that for other algorithms) or an
artifact of the algorithm's implementation (e.g. Bunch creates
decompositions that are more balanced than most algorithms).

====== Algorithm Restrictions ======
Since software clustering
algorithms are heuristic algorithms, their performance
may depend on the type of software system being clustered. Such dependencies
need to be documented here. For example, Bunch may not be well-suited
for event-driven systems.

====== Failed Assumptions ======
A description of ideas that did not
work very well while developing the algorithm. This information is
often omitted in publications but could be invaluable to the software
clustering researcher.

====== Detailed Algorithm Description ======
 The nuts and bolts of the
algorithm's implementation are presented here. This will often be the
only section of significant length in an algorithm description.