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Keywords:
software cost estimation; fuzzy set; elicitation; aggregation operator design
software cost estimation; fuzzy set; elicitation; aggregation operator design
Summary:
Parametric software cost estimation models are well-known and widely used estimation tools, and several fuzzy extensions have been proposed to introduce a explicit handling of imprecision and uncertainty as part of them. Nonetheless, such extensions do not consider two basic facts that affect the inputs of software cost parametric models: cost drivers are often expressed through vague linguistic categories, and in many cases cost drivers are better expressed in terms of aggregations of second-level drivers. In this paper, fuzzy set elicitation techniques are used as a tool to model vague categories expressing cost driver quantities, focusing on two well-known COCOMO cost drivers. The results clearly indicate that such fuzzy set modelling approach affects significantly the estimation outcomes. In addition, the empirical adjustment of the DOCU cost driver as an aggregation of second-level documentation artifact measures is used to illustrate the modelling of flexible aggregation in the context of parametric estimation. Fuzzy set elicitation and aggregation operator modelling combined provide a novel approach to extending fuzzy parametric models for software estimation, which can be used as a complement to existing approaches.
References:
[1] Bilgiç T., Türksen T.: Measurement of Membership Functions: Theoretical and Empirical Work. In: Handbook of Fuzzy Sets and Systems (D. Dubois and H. Prade, eds.), Vol. 1, Chapter 3, Fundamentals of Fuzzy Sets, Kluwer 1999, pp. 195–232
[2] Boehm B. W.: Software Engineering Economics. Prentice–Hall, Englewood Cliffs, NJ 1981 Zbl 0525.90034
[3] Boehm B., Abts, C., Chulani S.: Software Development Cost Estimation Approaches – A Survey. Technical Report USC-CSE-2000-505, Center for Software Engineering, University of California 2000 Zbl 1012.68568
[4] Chulani S., Clark B., Boehm, B., Steece B.: Calibration approach and results of the COCOMO II post – architecture model. In: Proc. 20th Annual Conference of the International Society of Parametric Analysts (ISPA) and 8th Annual Conference of the Society of Cost Estimating and Analysis (SCEA), 1998
[5] Crespo J. J., Sicilia M. A., Cuadrado J. J.: On fuzzy regression in software cost estimation models. In: Proc. 2003 ACM–IEEE Internat. Symposium on Empirical Software Engineering (ISESE’03)
[6] Crespo J. J., Sicilia M. A., Cuadrado J. J.: On the use of fuzzy regression in parametric software estimation models: Integrating imprecision in COCOMO cost drivers. WSEAS Trans. on Systems 1 (2004), 3, 96–101
[7] Crespo J. J., Sicilia M. A., Cuadrado J. J.: On aggregating second-level software estimation cost drivers: A usability cost estimation case study. In: Proc. Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU04)
[8] Cuadrado-Gallego J. J., Marbán O., Amescua A., García, L., Sánchez M.: The importance of rating level selection method to obtain accury estimations in parametric mathematical models. J. Cost Analysis and Management, Summer (2004), 14–20 DOI 10.1080/15411656.2004.10462245
[9] Dolado J. J.: On the problem of the software cost function. Information & Software Technology 43 (2001), 1, 61–72 DOI 10.1016/S0950-5849(00)00137-3
[11] Ghahramani B.: Software reliability analysis: a systems development model. Computers & Industrial Engrg. 45 (2003), (2), 295–305 DOI 10.1016/S0360-8352(03)00037-8
[12] Idri A., Abran, A., Khoshgoftaar T. M.: Fuzzy analogy: A new approach for software cost estimation. In: Current Trends in Software Measurement (Dumke and Abran, eds.), Shaker Publ., Aachen 2001, pp. 127–142
[13] Izyumov B., Kalinina, E., Wagenknecht M.: Software tools for regression analysis of fuzzy data. In: Proc. 9th Zittau Fuzzy Colloquium, Zittau 2001, pp. 221–229
[14] Kalinina E., Wagenknecht M.: Fuzzy regression analysis and application to a crisp model. In: Proc. 8th Zittau Fuzzy Colloquium, Zittau 2000, pp. 9–18
[15] McCall J. A., Richards P. K., Walters G. F.: Factors in Software Quality, Vol. 1–3. AD/A 049-014/015/055. Springfield 1977, VA: National Technical Information Service
[16] Musilek P., Pedrycz W., Succi, G., Reformat M.: Software cost estimation with fuzzy models. Appl. Comput. Rev. 8 (2000), 2, 24–29 DOI 10.1145/373975.373984
[18] Palomar D., Sicilia M. A.: Web page usability analysis based on vague perceptual concepts. In: Proc. IADIS WWW Conference 2004, to appear
[19] Rosch E.: Principles of Categorization. Readings in Cognitive Science, Erlbaum 1988, pp. 312–322
[20] Sicilia M. A., García, E., Calvo T.: An enquiry-based method for Choquet integral-based aggregation of interface sability parameters. Kybernetika 39 (2003), 601–614 MR 2042343
[21] Jr. O. Souza Lima, Farias P. P. M., Belchior A. D.: Fuzzy function point analysis. In: Proc. 4th European Conference on Software Measurement and ICT Control, Heidelberg 2001, pp. 161–172
[22] Welie M. Van, Veer G. C. van der, Eliëns A.: Breaking down usability. In: Proc. of Interact’99, pp. 613–620
[23] Xu Z., Taghi T. M., Khoshgoftaar M.: Identification of fuzzy models of software cost estimation. Fuzzy Sets and Systems 145 (2004), 1, 141–163 MR 2061944
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