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Linear combination of multiple case-based reasoning with optimized weight for software effort estimation

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Abstract

Since software development has become an essential investment for many organizations recently, both the software industry and academic communities are more and more concerned about a reliable and accurate estimation of the software development effort. This study puts forward six widely used case-based reasoning (CBR) methods with optimized weights derived from the particle swarm optimization (PSO) method to estimate the software effort. Meanwhile, four combination methods are adopted to assemble the results of independent CBR methods. The experiments are carried out using two datasets of software projects from Desharnais dataset and Miyazaki dataset. Experimental results show that different CBR methods can get the best results in different parameters settings, and there is not a best method for the software effort estimation among the six different CBR methods. Currently, combination methods proposed in this study outperform independent methods, and the weighted mean combination (WMC) method can get the better result.

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Authors and Affiliations

  1. Institute of Policy & Management, Chinese Academy of Sciences, Beijing, 100190, P.R. China

    Dengsheng Wu & Jianping Li

  2. Graduate University of Chinese Academy of Sciences, Beijing, 100190, P.R. China

    Dengsheng Wu

  3. China Electronics Standardization Institute, Beijing, 100007, P.R. China

    Yong Liang

Authors
  1. Dengsheng Wu
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  2. Jianping Li
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  3. Yong Liang
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Correspondence to Jianping Li.

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Wu, D., Li, J. & Liang, Y. Linear combination of multiple case-based reasoning with optimized weight for software effort estimation. J Supercomput 64, 898–918 (2013). https://doi.org/10.1007/s11227-010-0525-9

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  • DOI: https://doi.org/10.1007/s11227-010-0525-9

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