Legacy systems contain critical and complex business code that has been in use for a long time. This code is difficult to understand, maintain, and evolve, in large part due to crosscutting concerns: software system features, such as persistence, logging, and error handling, whose implementation is spread across multiple modules. Aspect-oriented
techniques separate crosscutting concerns from the base code, using separate modules called aspects and, thus, simplifying the legacy code. Aspect mining techniques identify aspect candidates so that the legacy code can be refactored into aspects.
This study investigated an automated aspect mining method in which a vector-space model clustering approach was used with model-based clustering. The vector-space model clustering approach has been researched for aspect mining using a number of different heuristic clustering methods and producing mixed results. Prior to this study,
this model had not been researched with model-based algorithms, even though they have grown in popularity because they lend themselves to statistical analysis and show results that are as good as or better than heuristic clustering methods.
This study investigated the effectiveness of model-based clustering for identifying aspects when compared against heuristic methods, such as k-means clustering and agglomerative hierarchical clustering, using six different vector-space models. The study's results indicated that model-based clustering can, in fact, be more effective than heuristic methods and showed good promise for aspect mining. In general, model-based algorithms performed better in not spreading the methods of the concerns across the multiple clusters but did not perform as well in not mixing multiple concerns in the same cluster. Model-based algorithms were also significantly better at partitioning the data such that, given an ordered list of clusters, fewer clusters and methods would need to be analyzed to find all the concerns. In addition, model-based algorithms automatically determined the optimal number of clusters, which was a great advantage over heuristic-based algorithms. Lastly, the study found that the new vector-space models performed better, relative to aspect mining, than previously defined vector-space models.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1280 |
| Date | 01 January 2011 |
| Creators | Rand McFadden, Renata |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
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