Figure 10 Detected instances of decorator and composite in JHotDraw.
Related Figures (12)
Fig. 2. Representation of pattern structure as graphs and matrices. Fig. 3. UML class diagrams of two system segments and a design pattern Fig. 4. Corresponding graphs for the UML diagrams shown in Fig. 3. (Letters within nodes are not labels but indicate the name of the correspondin: node.) Fig. 5. Adjacency matrices resulting from the corresponding graphs in Fig. 4. this case, both roles are involved in the association and the generalization matrix). segment 1, five edit operations in total are required (generalization graph: deletion of edges (B, A) and (C, B), deletion of node B, addition of edge (C, A), association graph: deletion of node B). On the other hand, the similarity matrices between the corresponding graphs of segment 1 and the pattern are Consequently, any generalization relationship between two classes will be considered as a strong candidate for the pattern, while the modified version of segment 1 will be considered a rather weak candidate. GeMpattern,segl = The two larger entries in the last matrix indicate the strong similarity between classes (A, 1) and (C, 2) of the corresponding UML diagrams for system segment 1 and the pattern, shown in Fig. 3. In contrast to the results from the inexact matching algorithm, which indicates that the pattern is much closer to the structure of segment 2, the similarity algorithm correctly identifies the pattern being implemented in the structure of segment 1. The NormScoreSpattern,seg2 Similarity matrix also indicates simi- larity between classes (a, 1) and (b, 2), which is reasonable since the generalization matrices of segment 2 and the pattern in Fig. 5 are the same, but the strength of similarity is lower due to the difference of their association matrices. Fig. 6. Handling of multiple inheritance. Fig. 8. Case b: Multiple instances of the same pattern in a subsystem. Fig. 7. Case a: Multiple instances of the same pattern in a subsystem Fig. 9. Threshold value for similarity scores. Pattern Detection Results Alexander Chatzigeorgiou received the diplo- ma in electrical engineering and the PhD degree in computer science from the Aristotle University of Thessaloniki, Greece, in 1996 and 2000, respectively. He is a lecturer in software engineering in the Department of Applied Infor- matics at the University of Macedonia, Thessa- loniki, Greece. From 1997 to 1999 he was with Intracom SA Greece, as a telecommunications software designer. His research interests are in software metrics, object-oriented design and low-power hardware/ software design. He is a member of the IEEE Computer Society. > For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib.
