More documents

Recommendations

Info

252 CAMELIA CHIS ĂLIT¸ Ă–CRET¸ U(1) AND ANDREEA VESCAN (1) the evolution of the fitness function (best, worse and average) recorded for each run within 20 chromosomes populations and 10 generations. The evolution of fitness function (recorded for the best individual in each run) for 200 generations and 100 individuals is depicted in Figure 1(b). (a) Experiment with 10 generations and (b) Experiment with 200 generations and 20 individuals with eleven mutated genes 100 individuals Figure 1. The evolution of fitness function (best, worse and average) and the best individual evolution While compared with the previous experiments we noted that we are getting a lower number of different solutions while cumulating the results obtained in all the 100 runs, but the quality (and the number) of these solutions is improving much more. In a first experiment the greatest value of the fitness function is 0.3508 (with 69 individuals with the fitness > 0.33) while in a second experiment this is not more than 0.3536 (55 individuals with the fitness > 0.33). But the best chromosome was found in the experiment with 200 generations and 20 individuals having the value 0.3562. The best individual obtained allows to improve the structure of the class hierarchy. Therefore, a new class is added as base class for PrintServer and FileServer and the print method is renamed, its signature is changed and it is moved to the new Server class. The correct access to the class fields by encapsulating them within their classes is enabled. 3.2. Results obtained by others. Fatiregun et al. [2] applied genetic algorithms to identify transformation sequences for a simple source code, with 5 transformation array, whilst we have applied 6 distinct refactorings to 23 entities. Seng et al. [6] apply a weighted multi-objective search, in which metrics are combined into a single objective function. An heterogeneous weighed approach is applied here, since the weight of software entities in the overall system and refactorings cost are studied. Mens et al. [5] propose the techniques to detect the implicit dependencies between refactorings. Their analysis helped to identify which refactorings are most suitable to LAN simulation case study. Our approach considers all relevant applying of the studied refactorings to all entities. 4. Conclusions and Future work The paper defines the MOORSP by treating the cost constraint as an objective and combining it with the effect objective. The results of a proposed weighted objective genetic algorithm on a experimental didactic case study are presented and compared with other recommended solutions for the similar problems.
THE MULTI-OBJECTIVE REFACTORING SELECTION PROBLEM 253 Current paper discusses the weighted multi-objective optimization, but the Pareto approach is a further step in current research since it proves to be more suitable when it is difficult to combine several objectives into a single aggregated fitness function. More, the cost may be interpreted as a constraint, with the further consequences. Acknowledgement. This material is based upon work supported by the Romanian National University Research Council under award PN-II no. ID 550/2007. References [1] S. Demeyer, D. Janssens, T. Mens, Simulation of a LAN, Electronic Notes in Theoretical Computer Science, 72 (2002), pp. 34-56. [2] D. Fatiregun, M. Harman, R. Hierons, Evolving transformation sequences using genetic algorithms, in 4th International Workshop on Source Code Analysis and Manipulation (SCAM 04), Los Alamitos, California, USA, IEEE Computer Society Press, 2004, pp. 65-74. [3] M. Fowler. Refactoring: Improving the Design of Existing Software. Addison Wesley, 1999. [4] Y. Kim, O.L. deWeck, Adaptive weighted-sum method for bi-objective optimization: Pareto front generation, in Structural and Multidisciplinary Optimization, MIT Strategic Engineering Publications, 29(2), 2005, pp. 149-158. [5] T. Mens, G. Taentzer, O. Runge, Analysing refactoring dependencies using graph transformation, Software and System Modeling, 6(3), 2007, pp. 269-285. [6] O. Seng, J. Stammel, D. Burkhart, Search-based determination of refactorings for improving the class structure of object-oriented systems, in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, M. Keijzer, M. Cattolico, eds., vol. 2, ACM Press, Seattle, Washington, USA, 2006, pp. 1909-1916. (1) Computer Science Department, Faculty of Mathematics and Computer Science, Babes¸-Bolyai University, Cluj-Napoca, Romania E-mail address: cretu@cs.ubbcluj.ro, avescan@cs.ubbcluj.ro
Page 1 and 2:
Invited Lectures CONTENTS H. Horace
Page 3 and 4:
P. V. Borza, O. Gui, D. Dumitrescu,
Page 5:
F. M. Boian, C. Aldea, On Evaluatin
Page 9 and 10:
KNOWLEDGE ENGINEERING: PRINCIPLES A
Page 11 and 12:
DECISION SUPPORT SYSTEM FOR SOFTWAR
Page 13 and 14:
Page 15 and 16:
TRANSFORMING PROCEDURAL INTO OBJECT
Page 17 and 18:
Page 19 and 20:
COMDEVALCO FRAMEWORK 191 3.1. Model
Page 21 and 22:
COMDEVALCO FRAMEWORK 193 [2] Czibul
Page 23 and 24:
RAPID PROTOTYPING OF CONVERSATIONAL
Page 25 and 26:
RAPID PROTOTYPING OF CONVERSATIONAL
Page 27 and 28:
(1) (1) (1)
Page 29 and 30: ∼
Page 31 and 32: EFFORT ESTIMATION BY ANALOGY USING
Page 33 and 34: EFFORT ESTIMATION BY ANALOGY USING
Page 35 and 36: SOFTWARE COST ESTIMATION MODEL BASE
Page 37 and 38: SOFTWARE COST ESTIMATION MODEL BASE
Page 39 and 40: KNOWLEDGE ENGINEERING: PRINCIPLES A
Page 41 and 42: ONTOLOGY DEVELOPMENT: A SOFTWARE EN
Page 45 and 46: DURATION ESTIMATION OF A WORK PACKA
Page 49 and 50: A FORMAL CONCEPT ANALYSIS APPROACH
Page 53 and 54: HIGH COUPLING DETECTION USING FUZZY
Page 57 and 58: EFFICIENT RECURSIVE PARALLEL PROGRA
Page 61 and 62: SKEPTICAL REASONING IN CONSTRAINED
Page 65 and 66: ALGEBRAIC MODEL FOR THE SYNCHRONOUS
Page 69 and 70: REVERSE ENGINEERING AND SIMULATION
Page 71 and 72: REVERSE ENGINEERING AND SIMULATION
Page 73 and 74: A PARALLELIZATION SCHEME OF SOME AL
Page 75 and 76: A PARALLELIZATION SCHEME OF SOME AL
Page 79: THE MULTI-OBJECTIVE REFACTORING SEL
Page 83 and 84: VIRTUAL REALITY REHABILITATION ENVI
Page 85: VIRTUAL REALITY REHABILITATION ENVI
show all

CONTENTS

Create successful ePaper yourself

Delete template?

Save as template?