SEKE 2012 Proceedings - Knowledge Systems Institute

V. APPLICATION OF GENETIC ALGORITHM
As previously mentioned, in practice the class diagram s in
the repository would usually have different number of classes
than the number of classes in the query class diagram. While
discussing the Name Similarity and Structure Sim ilarity
measures in the last sec tion, we have ass umed that the c lass
diagrams have equal number of classes. In this section we
explain how GA can be used to select an equal num ber of
classes from both diagrams. Wang et al [14] used GA to match
2 graphs having the sam e number of node s. In contrast, our
method can match graphs having different number of nodes.
A. Chromosome Design
Let A and B be two class diagram s having na a nd nb
classes respectively such that na nb. The task of choosing
how all the na classes of A will be mapped to na classes in B is
a combinatorial optimization problem. GA has been use d to
solve combinatorial optimization problems such as timetabling,
scheduling, Travelling Salesman Problem, Eight Queens Chess
problem and s o on. Fig. 2 shows a suitable encoding of a
chromosome to determ ine the mapping of all the na clas ses
from A to na classes in B.
Each gene in the chrom osome represents a class nu mber in
B. For example, from Fig. 2 we observe that the 1 st class in A is
mapped to the 5 th class in B, the 2 nd class in A is mapped to the
nb th class in B, the 3 rd class in A is mapped to the na th class in B
and so on. In t his way, we map the classes in A to a subset of
the classes in B. The selected classes in B maintain their class
relationships only if both cla sses involved in the relationship
were chosen as part of the chromosome.
B. Fitness Function
We use the similarity measure S given in equation 3 as our
fitness function. Since the value of S always ranges from 0 to 1
(where 0 im plies the highest level of sim ilarity), successive
generations of GA should produces less values of S compared
to previous ge nerations. This results in GA selecting (near)
optimal mappings from classes in the query class diagram to
those in repository class diagrams.
VI. EXPERIMENTS
In this section, we present the results of experiments carried
out using our proposed method. Our experiments focused
mainly on Structure Sim ilarity (SS), henc e the weight in
equation 3 was set to 1. Our objective was to deter mine the
efficiency of the proposed method in retrieving matching class
diagrams from the repository. Fig. 3 shows two class diagrams;
a query class diagram Q and a class diagram R from the
repository. As shown in the figure, Q is isomorphic to a s ubgraph
of R.
We used the proposed method to determine how m any
times the classes in Q were correctly mapped to the classes in
R. Maximum similarity is obtained when the value of the
fitness function is zero as de scribed in Section V. Fig. 4 shows
the mean and standard deviation of the fitness value over 500
successive generations. The experiment was re peated 100
times. After a few genera tions, the mean and standard
deviations of the fitness function stabiliz es, indicating that
there is no additional benefit in running GA further.
TABLE II.
DIFF MATRIX
AS AG CO DE GE RE IR NO
AS 0 0.11 0.11 0.45 0.45 0.66 0.77 1
AG 0.11 0 0.11 0.45 0.45 0.66 0.77 1
CO 0.11 0.11 0 0.45 0.45 0.66 0.77 1
DE 0.49 0.49 0.49 0 0.28 0.21 0.32 1
GE 0.49 0.49 0.49 0.28 0 0.49 0.6 1
RE 0.83 0.83 0.83 0.34 0.62 0 0.11 1
IR 1 1 1 0.51 0.79 0.17 0 1
NO 1 1 1 1 1 1 1 0
AS = ASSOCIATION, AG = AGGREGATION, CO = COMPOSITION, DE = DEPENDENCY, GE =
GENERALIZATION, RE = REALIZATION, IR = INTERFACE REALIZATION, NO = NO
RELATION
In addition, the mean value of the fitness function is often
sufficiently close to zero because the propos ed algorithm finds
exact matches most of the time. However, the standard
deviation of the fitness value is higher than the mean in most
cases. This is because the algorithm usually finds optimal
solutions, but obtains near optimal solutions at other times.
In another set of experim ents, we studied the im pact of
using GA in our algorithm. We replaced the GA component of
our algorithm with a random matching of query class diagrams
to repository class diagrams. The experiment was repeated
1,000 times. For the GA-based experiments, there were 100
individuals in each generation, and the m aximum number of
generations was 50. Thus, the maximum number of iterations
was 5,000 across all generations. In the case of the experiments
based on random matching of classes, the matchings were
based on randomly generated permutations. The random
permutations were generated and tested up to 5,000 times. As
in the case of GA, the search was abandoned as soon as an
optimal matching was f ound. The res ults shown in Fi g. 5
indicate that o ur GA-based algorithm usually finds optim al
class matchings in less num ber of iterations com pared with
random matching of classes. The figure shows that in 700 out
of the 1000 cases, our GA-based algorithm finds optim al
matchings after a few generations ( 1,500 iterations or
alternatively 15 generations). However, 30% of the time, GA
executes all the 5,000 iterations (50 generations ) and
determines near-optimal class matchings.
VII. CONCLUSION AND FUTURE WORK
We have described a method for retrieving UML class
diagrams from a software repository by using GA. Few
experiments were carried out to measure the effectiveness of
the proposed algorithm in d etecting structural sim ilarity. No
experiment was carried out re garding name similarity. Thus, it
is necessary to perform many more experiments to evaluate the
performance of the proposed method in t erms of precision,
recall, execution time and so on.
We have considered only cl ass names and class topology.
In the future, we hope to include class attr ibutes and operations
in the similarity measure. In a ddition, the technique will be
extended to deter mine similarity measures of other U ML
diagrams such as sequence diagrams and state chart diagrams.
The development of a tool to integrate our proposed technique
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