SEKE 2012 Proceedings - Knowledge Systems Institute

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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 739
as an add-in to popular UML m odeling software like Rational Rose and Enterprise Architect is also being considered. 1 2 3 4 . . na 5 nb na 2 3 Figure 2. Chromosome Encoding for GA Frequency 700 600 500 400 300 Genetic Algorithm Random Permutation 200 100 0 1 - 1500 1501-3000 3001-4500 4501-5000 Number of Iterations Figure 5. Number of required iterations in GA-based matching and random matching of classes. ACKNOWLEDGMENT The authors would like to acknowledge the support provided by the Deanship of Scientific Research at King Fahd University of Petroleu m and Minerals (KFU PM) under Research Grant 11-INF1633-04. Figure 3. Repository and Query Class Diagrams. a) Repository Class Diagram b) Query Class Diagram. Classes 1, 2, 3 and 4 from the Query Diagram can be mapped to Classes 2, 4, 6 and 5 in the Repository Diagram respectively. 0.25 0.2 0.15 0.1 0.05 Mean Standard Deviation 0 0 50 100 150 200 250 300 350 400 450 500 Number of Generations Figure 4. Mean and Standard Deviation of similarity value over 500 generations REFERENCES [1] Krueger C. W, “Software reuse,” ACM Comput. Surv. vol.24, 2, pp 131-183, 1992. [2] I. Sommerville, “Software engineering,” 9th ed., Addison-Wesley, 2010. [3] R. Rufai, “New structural similarity metrics for the UML, ” MS Thesis, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia, 2003. [4] K.. Robles, A. Fraga, J. Morat and J. Llorens, “Towards an ontology - based retrieval of UML class diagrams,” Inf. Softw. Technol. vol. 54, no. 1, pp. 72-86, 2012 [5] W. N. Robinson and H. G. Woo, "Finding reusable UML sequence diagrams automatically," IEEE Software, vol. 21, pp. 60-67, 2004. [6] A. Ahmed, “Functional similarity metric for UML Models, ” MS Thesis King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia, 2006. [7] P. Gomes et al, “Using WordNet for case-based retrieval of UML models”. AI Commun. vol. 17, no. 1, pp 13-23, 2004. [8] M.C. Blok and J. L. Cy bulski, J.L, "Reusing UML specifications in a constrained application do main," in Software Engineering Conference, 1998. Proceedings. 1998 Asia Pacific, 1998, pp.196-202. [9] M. Gupta, R. Singh, A. Tripathi, “Design pattern detection using inexact graph matching,” in International Conference on C ommunication and Computational Intelligence (INCOCCI), 2010, pp.211-217. [10] W. Park and D. Bae, “A two-stage framework for UML specification matching,” Inf. Softw. Technol., v ol. 53, no. 3, pp. 230-244, March 2011. [11] E. Bengoetxea, “Inexact graph matching using estimation of distribution algorithms,” Phd Dissertation, University of the Basque Country, 2002. [12] Z. Xing, E. Stroulia, “UMLDiff: an algorithm for object-oriented design differencing,” in Proceedings of t he 20th IEEE/A CM international Conference on Automated Software Engineering, 2005, pp. 54-65. [13] A. Hlaoui, S. Wang, “A new algor ithm for inexact graph matching,” in 16th International Conference on Pat tern Recognition, vol. 4, 2002, pp. 180- 183. [14] Y. Wang, K. Fan, and J. Hor ng, “Genetic-based search for errorcorrecting graph isomorphism,” IEEE Trans. on SM C, vol. 27, no. 4, pp.588-597, Aug 1997. 740
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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364
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
Page 675 and 676:
contribution of each study was made
Page 677 and 678:
assets, the plugin approach can be
Page 679 and 680:
Figure 3: PlugSPL Feature Model Edi
Page 681 and 682:
contributions from the several acto
Page 683 and 684:
o Guideline 6.4: Softgoal dependenc
Page 685 and 686:
descriptions must also b e configur
Page 687 and 688:
It means that throughout the develo
Page 689 and 690:
B. Instrumentation The experiment w
Page 691 and 692:
• External Validity: W e have con
Page 693 and 694:
II. THE PROPOSED TAXONOMY The taxon
Page 695 and 696:
sub-dimension is categorized as, co
Page 697 and 698:
A Proposal of Reference Architectur
Page 699 and 700:
Figure. 1. Reference Architecture M
Page 701 and 702:
A Variability Management Method for
Page 703 and 704:
C. Correctness of configuration fil
Page 705 and 706:
means patterns which are shown in s
Page 707 and 708:
Tool Support for Anomaly Detection
Page 709 and 710:
Figure 1. System overview of the SD
Page 711 and 712:
Once the datasets were evaluated us
Page 713 and 714:
Reconfiguration of Robot Applicatio
Page 715 and 716:
data dependencies required for keep
Page 717 and 718: Spacemaker: Practical Formal Synthe
Page 719 and 720: Fig. 1. The ecommerce object model.
Page 721 and 722: Fig. 3. Mapping diagram for Figure
Page 723 and 724: A formal support for incremental be
Page 725 and 726: machine may be empty which should l
Page 727 and 728: software, based on revised requirem
Page 729 and 730: A Process-Based Approach to Improvi
Page 731 and 732: Appropriate processes m ust support
Page 733 and 734: Figure 2. Reordered layers of the k
Page 735 and 736: Automatic Acquisition of isA Relati
Page 737 and 738: III. VALIDATING THE DIMENSION HEADE
Page 739 and 740: The table title and the dimension s
Page 741 and 742: A light weight alternative for OLAP
Page 743 and 744: i.d. subsets of cubes focused on se
Page 745 and 746: Match production rules Enterprise S
Page 747 and 748: A Tool for Visualization of a Knowl
Page 749 and 750: other ontology visualization tools
Page 751 and 752: shown at Figure 5. Objectives are s
Page 753 and 754: Rendering UML Activity Diagrams as
Page 755 and 756: a = O1 → a → O2 b = O2 → b
Page 757 and 758: ecordProblem → A → reproducePro
Page 759 and 760: umlTUowl - A Both Generic and Vendo
Page 761 and 762: The tool’s ability to deal with S
Page 763 and 764: Elem. UML Example D 12 Harmonizing
Page 765 and 766: 4) Associations: In the first test
Page 767: III. GRAPH REPRESENTATION OF CLASS
Page 771 and 772: 742
Page 773 and 774: 744
Page 775 and 776: 746
Page 777 and 778: her necessities. However, the progr
Page 779 and 780: Figure 3. Global Log. of terms. The
Page 781 and 782: two stages. The first stage is focu
Page 783 and 784: 754
Page 785 and 786: 756
Page 787 and 788: 758
Page 789 and 790: With the GDUC approach, we can mode
Page 791 and 792: Figure 6. Three proposals containin
Page 793 and 794: 764
Page 795 and 796: 766
Page 797 and 798: Proactive Two Way Mobile Advertisem
Page 799 and 800: information. If more than one adver
Page 801 and 802: Users can al so publish adv ertisem
Page 803 and 804: The COIN Platform: Supporting the M
Page 805 and 806: A-3
Page 807 and 808: Checking Contracts for AOP using XP
Page 809 and 810: Maicon B. da Silveira, 112 Aldo Dag
Page 811 and 812: Seyedehmehrnaz Mireslami, 70 Takao
Page 813 and 814: Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816: Desmond Greer Eric Gregoire Christi
Page 817 and 818: Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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