Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10664-008-9104-6
Title: A study of the non-linear adjustment for analogy based software cost estimation
Authors: Li, Y.F.
Xie, M. 
Goh, T.N. 
Keywords: Analogy based estimation
Artificial datasets
Artificial neural network
Case based reasoning
Non-linear adjustment
Software cost estimation
Issue Date: Dec-2009
Source: Li, Y.F., Xie, M., Goh, T.N. (2009-12). A study of the non-linear adjustment for analogy based software cost estimation. Empirical Software Engineering 14 (6) : 603-643. ScholarBank@NUS Repository. https://doi.org/10.1007/s10664-008-9104-6
Abstract: Cost estimation is one of the most important but most difficult tasks in software project management. Many methods have been proposed for software cost estimation. Analogy Based Estimation (ABE), which is essentially a case-based reasoning (CBR) approach, is one popular technique. To improve the accuracy of ABE method, several studies have been focusing on the adjustments to the original solutions. However, most published adjustment mechanisms are based on linear forms and are restricted to numerical type of project features. On the other hand, software project datasets often exhibit non-normal characteristics with large proportions of categorical features. To explore the possibilities for a better adjustment mechanism, this paper proposes Artificial Neural Network (ANN) for Non-linear adjustment to ABE (NABE) with the learning ability to approximate complex relationships and incorporating the categorical features. The proposed NABE is validated on four real world datasets and compared against the linear adjusted ABEs, CART, ANN and SWR. Subsequently, eight artificial datasets are generated for a systematic investigation on the relationship between model accuracies and dataset properties. The comparisons and analysis show that non-linear adjustment could generally extend ABE's flexibility on complex datasets with large number of categorical features and improve the accuracies of adjustment techniques. © 2009 Springer Science+Business Media, LLC.
Source Title: Empirical Software Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/62965
ISSN: 13823256
DOI: 10.1007/s10664-008-9104-6
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