| mailto:
| creator |
Christl, Andreas
| | date |
2005-01-10
| | | | description |
117 pages
| |
A significant aspect in applying the Software Reflexion Model
analysis is mapping of components found in the source code onto the
conceptual components defined in the hypothesized architecture. To
date, this mapping is done manually, which requires a lot of work
for large software systems. This thesis evaluates if and how cluster
analysis can leverage the manual mapping of source code artifacts.
For this evaluation, the HuGMe method has been developed, in which
assets of existing clustering techniques are combined and applied to
support the user in the mapping activity. The result is a
semi-automated mapping approach that accommodates the automatic
clustering of the source model with the user's hypothesized
knowledge about the system's architecture. In addition, a
user-interface is designed to support the semi-automated mapping of
HuGMe.
The core of HuGMe is what I call a supportive clustering algorithm.
The term 'supportive' means that the task of the algorithm
is not only to automatically cluster entities but to support the
user in the efforts to achieve a correct and complete map. The
clustering algorithm maps only those source code artifacts without
user-interaction for which a mapping decision is 'easy
enough' to be made automatically. For all other source code
components, where such an automatic mapping is not possible, the
clustering algorithm provides suggestions for the user to which
subsystem the component might belong.
This thesis also presents a case study in which the cluster analysis
of HuGMe is successfully applied to extend a partial map of a
real-world software application. Thus confirming the benefit cluster
analysis brings into the mapping activity.
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