Open Access Open Access  Restricted Access Subscription or Fee Access

A New Approach for Requirements Management and Component Retrieval in A Cbd Based Software Development Using 2-Tuple Fuzzy Linguistic Approach

K.S Jasmine, Dr. R. Vasantha

Abstract


Requirements management is a complex process in component based software development. A problem of requirements management is that requirements in general are incomplete, imprecise and contradictory. In an in-house development, the main objective is to implement a system, which will satisfy the requirements as far as possible within a specified framework of different constraints. In component based development, the fundamental approach is the reuse of existing components. The process of engineering requirements is much more complex as the possible candidate components usually lacking one or more features which meet the system requirements exactly. In addition, even if some components are individually well suited to the system, it is not necessary that they do not function optimally in combination with others in the system- or perhaps not at all. These constraints may require another approach in requirements engineering – an analysis of the feasibility of requirements in relation to the components available and the consequent modification of requirements. As there are many uncertainties in the process of component selection there is a need for a strategy for managing risks in the components selection and evolution process [31][32]. In this study, we are suggesting an efficient component retrieval system for a component search in a CBD based software development using 2-tuple fuzzy linguistic approach. Component retrieval is an activity that implies to achieve component that better fulfill the user requirements. For achieving this activity a component Retrieval System uses matching functions, which specify the degree of relevance of a component with respect to a user search for a component. Assuming linguistic weighted queries we present a new linguistic matching function, which is defined using a 2-tuple fuzzy linguistic approach [2] based on the functional description of the component. This new 2-tuple linguistic matching function can be interpreted as a tuning of that defined in [3] using an ordinal linguistic approach. We show that it simplifies the processes of computing in the retrieval activity, avoids requirements compromise in the final product, and consequently, can help to improve the users’ satisfaction and the quality of the product.


Keywords


Software Reuse, CBD, Fuzzy Information Retrieval, Component Search, Linguistic Modeling, Weighted Queries.

Full Text:

PDF

References


F. Herrera and L. Martínez, A 2-tuple fuzzy linguistic representation model for computing with words, IEEE Transactions on Fuzzy Systems 8:6 (2000)

E. Herrera-Viedma, Modeling the retrieval process for an information retrieval system using an ordinal fuzzy linguistic approach, Journal of the American Society for Information Science and Technology 52:6 (2001) 460-475

E. Herrera-Viedma, An information retrieval system with ordinal linguistic weighted queries based on two weighting elements, Int. J.of Uncertainty, Fuzziness and Knowledge-Based Systems 9 (2001)77-88.

A. Bookstein, Fuzzy request: An approach to weighted Boolean searches, J. of the American Society for Information Science 31(1980) 240-247.

G. Bordogna, C. Carrara and G. Pasi, Query term weights as constraints in fuzzy information retrieval, Information Processing & Management 27 (1991)

G. Bordogna and G. Pasi, Linguistic aggregation operators of selection criteria in fuzzy information retrieval, Int. J. of Intelligent Systems 10 (1995) 233-248.

D. Buell and D.H. Kraft, Threshold values and Boolean retrieval systems, Information Processing & Management 17 (1981) 127-136.

D. Buell and D.H. Kraft, A model for a weighted retrieval system, J. of the American Society for Information Science 32 (1981) 211-216.

C.S. Cater and D.H. Kraft, A generalization and clarification of the Waller-Kraft wish list, information Processing & Management 25 (1989) 15-25.

D.H. Kraft and D.A. Buell, Fuzzy sets and generalized Boolean retrieval systems, Int.l J. of Man-Machine Studies 19 (1983) 45-56.

W.G. Waller and D.H. Kraft, A mathematical model of a weighted Boolean retrieval system, Information Processing & Management 15(1979) 235-245.

R.R. Yager, A Hierarchical Document Retrieval Language, Information Retrieval 3 (2000) 357-377.

R.R. Yager, A note on weighted queries in information retrieval system, J. of American Society of Information Science 38 (1987) 23-24.

G. Bordogna and G. Pasi, A fuzzy linguistic approach generalizing Boolean information retrieval: A model and its evaluation, J. of the American Society for Information Science 44 (1993) 70-82.

D.H. Kraft, G. Bordogna and G. Pasi, An extended fuzzy linguistic approach to generalize Boolean information retrieval, Information Sciences 2 (1994) 119-134.

E. Herrera-Viedma, O. Cordón, M. Luque, A.G. Lopez, A.M. Muñoz, A model of fuzzy linguistic IRS based on multi-granular linguistic information,Int. J. of Approximate Reasoning 34 (2003) 221-239.

G. Bordogna and G. Pasi. An ordinal information retrieval model, Int.J. of Uncertainty, Fuzziness and Knowledge-Based Systems 9 (2001) 63-76.

L.A. Zadeh, The concept of a linguistic variable and its applications to approximate reasoning. Part I, Information Sciences 8 (1975) 199-249, Part II, Information Sciences 8 (1975) 301-357, Part III, Information Sciences 9 (1975) 43-80.

F. Herrera and E. Herrera-Viedma, Aggregation operators for linguistic weighted information, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 27 (1997) 646-656.

F. Herrera, E. Herrera-Viedma and J.L. Verdegay, Direct approach processes in group decision making using linguistic OWA operators,Fuzzy Sets and Systems, 79 (1996) 175-190.

R.R. Yager, An approach to ordinal decision-making, Int. J. of Approximate Reasoning 12 (1995) 237-261.

P.P. Bonissone and K.S. Decker, Selecting uncertainty calculi and granularity: An experiment in trading-off precision and complexity,in: L.H. Kanal and J.F. Lemmer, Eds., Uncertainty in Artificial Intelligence (North-Holland,1986) 217-247.

R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Transactions on Systems, Man,and Cybernetics 18 (1988) 183-190.

S. Miyamoto, Fuzzy Sets in Information Retrieval and Cluster Analysis (Kluwer Academic Publishers, 1990).

W.B.Frakes and B.A Nejmeh, An information system for software reuse, Software Reuse: Emerging Technology, IEEE, CS Press,1990,pp.142-151.

C.W. Krueger, Software reuse, ACM Computing Surveys, ACM Press, vol.24, no.2, pp.131-183.June 1992.

Y.S.Yoelle S.Maarek, D.M.Berry, and G.E. Kaiser, An information retrieval approach for automatically constructing software libraries,IEEE Trans.Software Engineering, vol.17, no.8, pp.800-813,Aug.1991.

G.salton and M. McGill, Introduction to Modern information retrieval, McGraw-Hill, New York, 1983.

L.S.Sorumgard, G.Sindre and F. Stokke, Experiences from application of a faceted classification scheme, Selected papers from the 2nd Int’l Workshop on software reusability Advances in software,pp.116-124, ReuseLucca, Italy, March 1993,IEEE CS Press.

D.Blair and M.E. Maron, An evaluation of retrieval effectiveness for a full -text, Document-Retrieval System Comm.ACM, vol.35, no.4,pp.289-299, March 1985.

Kotonya G. and Rashid A., A strategy for Managing Risks in Component-based Software Development, 27th Euromicro Conference 2001 Proceedings, IEEE Computer society, 2001, pp. 12-21

Heineman G. and Councill W. Component-based Software Engineering, Putting the Pieces Together. Addison Wesley, 2001


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.