A legacy software system can be taken to consist of N methods which contain within their implementations the intended activities and functions of the system. These activities and functions are referred to as concerns. Some of these concerns are typically implemented and used in multiple methods throughout the system and these are deemed to be crosscutting concerns. Through the use of an aspect-oriented programming paradigm, the implementation and use of these crosscutting concerns can be abstracted into aspects. In order to refactor the system, the process of aspect mining is carried out to identify the crosscutting concerns in the software system. Once identified, the crosscutting concerns can then be refactored into aspects.
Clustering-based aspect mining techniques make use of a vector space model to represent the source code to be mined. In this investigation, the individual methods of the software system were represented by a d-dimensional vector by mapping a method M to the vector V where the components of the vector V were values derived from applying a source code metric to each method M. These vector space models were then processed through the k-means++ clustering algorithm and the resulting cluster configurations were then evaluated to assess the quality of the results with respect to the identification of crosscutting concerns.
This research studied the effect that the number of dimensions of a vector space model has
on the results of a clustering-based aspect mining algorithm. Several vector space models
were defined and principal component analysis was used to reduce the dimensionality of the models. Each of the models was processed multiple times through the aspect mining algorithm and the distributions of the collected measures were tested for statistically significant differences using the Wilcoxon rank sum test. The results indicate that changes in the number of dimensions of a vector space model can produce significant effects in the collected measures. In addition, the measures used to assess the performance of an aspect mining process need to be analyzed for underlying relationships.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1325 |
| Date | 01 January 2011 |
| Creators | Tribbey, William |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
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