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Soft Computing

, Volume 22, Issue 16, pp 5299–5310 | Cite as

Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation

  • Dengsheng Wu
  • Jianping Li
  • Chunbing Bao
Focus

Abstract

Software effort estimation (SEE) is the process of forecasting the effort required to develop a new software system, which is critical to the success of software project management and plays a significant role in software management activities. This study examines the potentials of the SEE method by integrating particle swarm optimization (PSO) with the case-based reasoning (CBR) method, where the PSO method is adopted to optimize the weights in weighted CBR. The experiments are implemented based on two datasets of software projects from the Maxwell and Desharnais datasets. The effectiveness of the proposed model is compared with other published results in terms of the performance measures, which are MMRE, Pred(0.25), and MdMRE. Experimental results show that the weighed CBR generates better software effort estimates than the unweighted CBR methods, and PSO-based weighted grey relational grade CBR achieves better performance and robustness in both datasets than other popular methods.

Keywords

Software effort estimation Case-based reasoning Particle swarm optimization Weight optimization 

Notes

Acknowledgements

This study was funded by the National Natural Science Foundation of China (71201156, 71425002, 71571179) and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2013112, 2012137).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Institutes of Science and DevelopmentChinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.School of Public Policy and ManagementUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China

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