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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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References

  1. Boehm BW, Papaccio PN (1988) Understanding and controlling software costs. IEEE Trans Softw Eng 14:1462–1477

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  4. Huang S, Chiu N (2006) Optimization of analogy weights by genetic algorithm for software effort estimation. Inf Softw Technol 48:1034–1045

    Article  Google Scholar 

  5. Li J, Chen Z, Wei L, Xu W, Kou G (2007) Feature selection via least squares support feature machine. Int J Inf Technol Decis Making 6:671–686

    Article  MATH  Google Scholar 

  6. Li J, Wei L, Li G, Xu W (2010) An evolution-strategy based multiple kernels multi-criteria programming approach: the case of credit decision making. Decis Support Syst. doi:10.1016/j.dss.2010.11.022

  7. Huang S, Chiu N, Chen L (2008) Integration of the grey relational analysis with genetic algorithm for software effort estimation. Eur J Oper Res 188:898–909

    Article  MATH  Google Scholar 

  8. Li J, Wu D, Cai C, Xu W (2010) Case based reasoning method with optimized weight derived by particle swarm optimization for software effort estimation. J Comput Syst Sci (forthcoming)

  9. Medeiros J, Schirru R (2008) Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm. Ann Nucl Energy 35:576–582

    Article  Google Scholar 

  10. Li Q, Wang Q, Yang Y, Li M (2008) Reducing biases in individual software effort estimations: a combining approach. In: International symposium on empirical software engineering and measurement. ACM Press, New York, pp 223–232

    Google Scholar 

  11. Peng Y, Wang G, Wang H (2010) User preferences based software defect detection algorithms selection using MCDM. Inf Sci. doi:10.1016/j.ins.2010.04.019

  12. Peng Y, Kou G, Wang H, Ko FIS (2009) Empirical evaluation of classifiers for software risk management. Int J Inf Technol Decis Making 8:749–767

    Article  MATH  Google Scholar 

  13. MacDonell S, Shepperd M (2003) Combining techniques to optimize effort predictions in software project management. J Syst Softw 66:91–98

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Chiu N, Huang S (2007) The adjusted analogy-based software effort estimation based on similarity distances. J Syst Softw 80:628–640

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Stamelos L, Angelis L, Morisio M, Sakellarise E, Bleris GL (2003) Estimating the development cost of custom software. Inf Manag 40:729–741

    Article  Google Scholar 

  18. Lin R, Wang Y, Wu C, Chuang C (2009) Developing a business failure prediction model via RST, GRA and CBR. Expert Syst Appl 36:1593–1600

    Article  Google Scholar 

  19. Khoshgoftaar T, Seliya N, Sundaresh N (2006) An empirical study of predicting software faults with case-based reasoning. Softw Qual J 14:85–111

    Article  Google Scholar 

  20. Cho S, Hong H, Ha B (2010) A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: for bankruptcy prediction. Expert Syst Appl 37:3482–3488

    Article  Google Scholar 

  21. Kumar K, Singh Y, Sanyal S (2009) Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU. Expert Syst Appl 36:65–71

    Article  Google Scholar 

  22. Li H, Sun J (2009) Gaussian case-based reasoning for business failure prediction with empirical data in China. Inf Sci 179:89–108

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Mendes E, Lokan C, Harrison R, Triggs C (2005) A replicated comparison of cross-company and within-company effort estimation models using the isbsg database. In: 11th IEEE international software metrics symposium (METRICS’05). IEEE Press, New York, pp 36–45

    Chapter  Google Scholar 

  25. Bates J, Granger C (1969) The combination of forecasts. OR 20:451–468

    Article  Google Scholar 

  26. Clemen RT (1989) Combining forecasts: a review and annotated bibliography. Int J Forecast 5:559–583

    Article  Google Scholar 

  27. Rapach D, Strauss J (2008) Forecasting US employment growth using forecast combining methods. J Forecast 27:75–93

    Article  MathSciNet  Google Scholar 

  28. Marchetti D, Parigi G (2000) Energy consumption, survey data and the prediction of industrial production in Italy: a comparison and combination of different models. J Forecast 19:419–440

    Article  Google Scholar 

  29. Wong K, Song H, Witt S, Wu D (2007) Tourism forecasting: to combine or not to combine? Tour Manag 28:1068–1078

    Article  Google Scholar 

  30. Lyu M, Nikora A (1992) Applying reliability models more effectively. IEEE Softw 9:43–52

    Article  Google Scholar 

  31. Hsu C, Rodas N, Huang C, Peng K (2010) A study of improving the accuracy of software effort estimation using linearly weighted combinations. In: IEEE 34th annual computer software and applications conference workshops. IEEE Press, New York, pp 98–103

    Chapter  Google Scholar 

  32. Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7:39–59

    Google Scholar 

  33. Mair C, Kadoda G, Lefley M, Phalp K, Schofield C, Shepperd M, Webster S (2000) An investigation of machine learning based prediction systems. J Syst Softw 53:23–29

    Article  Google Scholar 

  34. Miyazaki Y, Terakado M, Ozaki K, Nozaki H (1994) Robust regression for developing software estimation models. J Syst Softw 27:3–16

    Article  Google Scholar 

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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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