Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/21341
Title: | Feature-Oriented Design Pattern Detection in Object-Oriented Systems |
Authors: | Hu, Lei |
Advisor: | Sartipi, Kamran |
Department: | Computing and Software |
Keywords: | feature-oriented, design pattern detection, object-oriented systems, two-phases, software |
Publication Date: | Jul-2007 |
Abstract: | <p> Identifying design pattern instances within an existing software system can support understanding and reuse of the system functionality. Moreover, incorporating behavioral features through task scenario into the design pattern recovery would enhance both the scalability of the process and the usefulness of the design pattern instances. In this context, we present a novel method for recovering design pattern instances from the implementation of system behavioral features through a semi-automatic and multi-phase reverse engineering process.</p> <p> The proposed method consists of a feature-oriented dynamic analysis and a two-phase design pattern detection process. The feature-oriented dynamic analysis works on the software system behavioral features' run-time information and produces a mapping between features and their realization at class level. In the two-phase design pattern detection process, we employ an approximate matching and a structural matching to detect the instances of the target design pattern described in our proposed Pattern Description Language (PDL), which is an XML-based design pattern description language. The correspondence between system features and the identified design pattern instances can facilitate the construction of more reusable and configurable software components. Our target application domain is an evolutionary development of software product line which emphasizes on reusing software artifacts to construct a reference architecture for several similar products. We have implemented a prototype toolkit and conducted experimentations on three versions of JHotDraw systems to evaluate our approach.</p> |
URI: | http://hdl.handle.net/11375/21341 |
Appears in Collections: | Digitized Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Hu_Lei_2007Jul_Masters..pdf | 4.18 MB | Adobe PDF | View/Open |
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