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Analogy-based software effort estimation using Fuzzy numbers
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DOI：10.1016/j.jss.2010.09.028.png)
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Abstract
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Background: Early stage software effort estimation is a crucial task for project bedding and feasibility studies. Since collected data during the early stages of a software development lifecycle is always imprecise and uncertain, it is very hard to deliver accurate estimates. Analogy-based estimation, which is one of the popular estimation methods, is rarely used during the early stage of a project because of uncertainty associated with attribute measurement and data availability. Aims: We have integrated analogy-based estimation with Fuzzy numbers in order to improve the performance of software project effort estimation during the early stages of a software development lifecycle, using all available early data. Particularly, this paper proposes a new software project similarity measure and a new adaptation technique based on Fuzzy numbers. Method: Empirical evaluations with Jack-knifing procedure have been carried out using five benchmark data sets of software projects, namely, ISBSG, Desharnais, Kemerer, Albrecht and COCOMO, and results are reported. The results are compared to those obtained by methods employed in the literature using case-based reasoning and stepwise regression. Results: In all data sets the empirical evaluations have shown that the proposed similarity measure and adaptation techniques method were able to significantly improve the performance of analogy-based estimation during the early stages of software development. The results have also shown that the proposed method outperforms some well know estimation techniques such as case-based reasoning and stepwise regression. Conclusions: It is concluded that the proposed estimation model could form a useful approach for early stage estimation especially when data is almost uncertain. (C) 2010 Elsevier Inc. All rights reserved.
Keywords:
Early stage software effort estimation
Cost estimation
Estimation by analogy
Similarity measurement
Generalized Fuzzy numbers
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