The organization of software systems into subsystems is usually based on the
constructs of packages or modules and has a major impact on the maintainability of
the software. However, during software evolution, the organization of the system is
subject to continual modification, which can cause it to drift away from the original
design, often with the effect of reducing its quality.
A number of techniques for evaluating a system's maintainability and for controlling
the effort required to conduct maintenance activities involve software clustering.
Software clustering refers to the partitioning of software system components
into clusters in order to obtain both exterior and interior connectivity between these
components. It helps maintainers enhance the quality of software modularization
and improve its maintainability.
Research in this area has produced numerous algorithms with a variety of
methodologies and parameters. This thesis presents a novel ensemble approach
that synthesizes a new solution from the outcomes of multiple constituent clustering
algorithms. The main principle behind this approach derived from machine
learning, as applied to document clustering, but it has been modified, both conceptually
and empirically, for use in software clustering. The conceptual modifications
include working with a variable number of clusters produced by the input algorithms
and employing graph structures rather than feature vectors. The empirical
modifications include experiments directed at the selection of the optimal cluster merging criteria. Case studies based on open source software systems show that
establishing cooperation between leading state-of-the-art algorithms produces better
clustering results compared with those achieved using only one of any of the
algorithms considered.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/8543 |
| Date | 18 June 2014 |
| Creators | Ibrahim, Ahmed Fakhri |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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