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

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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.

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References

  • Angelis L, Stamelos I (2000) A simulation tool for efficient analogy based cost estimation. Empir Softw Eng 5(1):35–68

    Article  Google Scholar 

  • Auer M, Trendowicz A, Graser B et al (2006) Optimal project feature weights in analogy-based cost estimation: improvement and limitations. IEEE Trans Softw Eng 32(2):83–92

    Article  Google Scholar 

  • Azzeh M, Neagu D, Cowling PI (2010) Fuzzy grey relational analysis for software effort estimation. Empir Softw Eng 15(1):60–90

    Article  Google Scholar 

  • Bardsiri VK, Jawawi DNA, Hashim SZM et al (2013) A PSO-based model to increase the accuracy of software development effort estimation. Softw Qual J 21(3):501–526

    Article  Google Scholar 

  • Boehm B, Abts C, Chulani S (2000) Software development cost estimation approaches: a survey. Ann Softw Eng 10(1–4):177–205

    Article  MATH  Google Scholar 

  • Burgess CJ, Lefley M (2001) Can genetic programming improve software effort estimation? A comparative evaluation. Inf Softw Technol 43(14):863–873

    Article  Google Scholar 

  • Cerpa N, Verner JM (2009) Why did your project fail? Commun ACM 52(12):130–134

    Article  Google Scholar 

  • Chiu NH, Huang SJ (2007) The adjusted analogy-based software effort estimation based on similarity distances. J Syst Softw 80(4):628–640

    Article  Google Scholar 

  • Dejaeger K, Verbeke W, Martens D et al (2012) Data mining techniques for software effort estimation: a comparative study. IEEE Trans Softw Eng 38(2):375–397

    Article  Google Scholar 

  • El Emam K, Benlarbi S, Goel N et al (2001) Comparing case-based reasoning classifiers for predicting high risk software components. J Syst Softw 55(3):301–320

    Article  Google Scholar 

  • Georgopoulos VC, Stylios CD (2008) Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions. Soft Comput 12(2):191–199

    Article  Google Scholar 

  • Heemstra FJ (1992) Software cost estimation. Inf Softw Technol 34(10):627–639

    Article  Google Scholar 

  • Hsu C-J, Huang C-Y (2011) Comparison of weighted grey relational analysis for software effort estimation. Softw Qual J 19(1):165–200

    Article  Google Scholar 

  • Huang SJ, Chiu NH (2006) Optimization of analogy weights by genetic algorithm for software effort estimation. Inf Softw Technol 48(11):1034–1045

    Article  Google Scholar 

  • Huang XS, Ho D, Ren J et al (2006) A soft computing framework for software effort estimation. Soft Comput 10(2):170–177

    Article  Google Scholar 

  • Huang SJ, Chiu NH, Chen LW (2008) Integration of the grey relational analysis with genetic algorithm for software effort estimation. Eur J Oper Res 188(3):898–909

    Article  MATH  Google Scholar 

  • Idri A, Amazal FA, Abran A (2015) Analogy-based software development effort estimation: a systematic mapping and review. Inf Softw Technol 58:206–230

    Article  Google Scholar 

  • Idri A, Abnane I, Abran A (2016) Missing data techniques in analogy-based software development effort estimation. J Syst Softw 117:595–611

    Article  Google Scholar 

  • Jeffery R, Ruhe M, Wieczorek I (2000) A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data. Inf Softw Technol 42(14):1009–1016

    Article  Google Scholar 

  • Jorgensen M (2004) A review of studies on expert estimation of software development effort. J Syst Softw 70(1–2):37–60

    Article  Google Scholar 

  • Jorgensen M, Shepperd M (2007) A systematic review of software development cost estimation studies. IEEE Trans Softw Eng 33(1):33–53

    Article  Google Scholar 

  • Kaya I (2009) A genetic algorithm approach to determine the sample size for attribute control charts. Inf Sci 179(10):1552–1566

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks proceedings, pp 1942–1948

  • Keung J, Kocaguneli E, Menzies T (2013) Finding conclusion stability for selecting the best effort predictor in software effort estimation. Autom Softw Eng 20(4):543–567

    Article  Google Scholar 

  • Kolodner JL (1992) An introduction to case-based reasoning. Artif Intell Rev 6(1):3–34

    Article  Google Scholar 

  • Li YF, Xie M, Go TN (2009a) A study of mutual information based feature selection for case based reasoning in software cost estimation. Expert Syst Appl 36(3):5921–5931

    Article  Google Scholar 

  • Li YF, Xie M, Goh TN (2009b) A study of project selection and feature weighting for analogy based software cost estimation. J Syst Softw 82(2):241–252

    Article  Google Scholar 

  • Liang C, Gu D, Bichindaritz I et al (2012) Integrating gray system theory and logistic regression into case-based reasoning for safety assessment of thermal power plants. Expert Syst Appl 39(5):5154–5167

    Article  Google Scholar 

  • Liu Y, Zhu X, Zhang J et al (2004) Application of particle swarm optimization algorithm for weighted fuzzy rule-based system. In: 30th annual conference of IEEE, pp 2188–2191

  • Mair C, Kadoda G, Lefley M et al (2000) An investigation of machine learning based prediction systems. J Syst Softw 53(1):23–29

    Article  Google Scholar 

  • Maxwell K (2002) Applied statistics for software managers. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Medeiros J, Schirru R (2008) Identification of nuclear power plant transients using the particle swarm optimization algorithm. Ann Nucl Energy 35(4):576–582

    Article  Google Scholar 

  • Nanni L, Lumini A (2009) Particle swarm optimization for prototype reduction. Neurocomputing 72(4–6):1092–1097

    Article  Google Scholar 

  • Phannachitta P, Keung J, Monden A et al (2017) A stability assessment of solution adaptation techniques for analogy-based software effort estimation. Empir Softw Eng 22(1):474–504

    Article  Google Scholar 

  • Sehra SK, Brar YS, Kaur N et al (2017) Research patterns and trends in software effort estimation. Inf Softw Technol 91:1–21

    Article  Google Scholar 

  • Sentas P, Angelis L, Stamelos I et al (2005) Software productivity and effort prediction with ordinal regression. Inf Softw Technol 47(1):17–29

    Article  Google Scholar 

  • Sha DY, Hsu CY (2008) A new particle swarm optimization for the open shop scheduling problem. Comput Oper Res 35(10):3243–3261

    Article  MATH  Google Scholar 

  • Shepperd M, Schofield C (1997) Estimating software project effort using analogies. IEEE Trans Softw Eng 23(11):736–743

    Article  Google Scholar 

  • Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation, p 1953

  • Stamelos L, Angelis L, Morisio M et al (2003) Estimating the development cost of custom software. Inf Manage 40(8):729–741

    Article  Google Scholar 

  • Urbanek T, Prokopova Z, Silhavy R et al (2015) Prediction accuracy measurements as a fitness function for software effort estimation. SpringerPlus 4(1):1–17

    Article  Google Scholar 

  • Wallace L, Keil M, Rai A (2004) How software project risk affects project performance: an investigation of the dimensions of risk and an exploratory model. Decis Sci 35(2):289–321

    Article  Google Scholar 

  • Wang Y, Yang Y (2009) Particle swarm optimization with preference order ranking for multi-objective optimization. Inf Sci 179(12):1944–1959

    Article  MathSciNet  Google Scholar 

  • Wang X, Yang J, Teng X et al (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett 28(4):459–471

    Article  Google Scholar 

  • Wang JY, Chang TP, Chen JS (2009) An enhanced genetic algorithm for bi-objective pump scheduling in water supply. Expert Syst Appl 36(7):10249–10258

    Article  Google Scholar 

  • Zhang JR, Zhang J, Lok TM et al (2007) A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Appl Math Comput 185(2):1026–1037

    MATH  Google Scholar 

  • Zhu H, Zhao J, Xu Y et al (2016) Interval-valued belief rule inference methodology based on evidential reasoning-IRIMER. Int J Inf Technol Decis Mak 15(6):1345–1366

    Article  Google Scholar 

Download references

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).

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Authors and Affiliations

  1. Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Dengsheng Wu, Jianping Li & Chunbing Bao

  2. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Dengsheng Wu, Jianping Li & Chunbing Bao

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  1. Dengsheng Wu
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  2. Jianping Li
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  3. Chunbing Bao
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Correspondence to Jianping Li.

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

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Communicated by X. Li.

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Wu, D., Li, J. & Bao, C. Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation. Soft Comput 22, 5299–5310 (2018). https://doi.org/10.1007/s00500-017-2985-9

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