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

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.

[1]  Roberto Schirru,et al.  Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm , 2008 .

[2]  Hui Li,et al.  Gaussian case-based reasoning for business failure prediction with empirical data in China , 2009, Inf. Sci..

[3]  Jianping Li,et al.  An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making , 2011, Decis. Support Syst..

[4]  Honggang Wang,et al.  User preferences based software defect detection algorithms selection using MCDM , 2012, Inf. Sci..

[5]  Magne Jørgensen,et al.  A Systematic Review of Software Development Cost Estimation Studies , 2007 .

[6]  F. J. Heemstra,et al.  Software cost estimation , 1992, Inf. Softw. Technol..

[7]  Qi Li,et al.  Reducing biases in individual software effort estimations: a combining approach , 2008, ESEM '08.

[8]  Khaled El Emam,et al.  Comparing case-based reasoning classifiers for predicting high risk software components , 2001, J. Syst. Softw..

[9]  Kevin K. F. Wong,et al.  Tourism forecasting: To combine or not to combine? , 2007 .

[10]  Keith Phalp,et al.  An investigation of machine learning based prediction systems , 2000, J. Syst. Softw..

[11]  Ioannis Stamelos,et al.  Estimating the development cost of custom software , 2003, Inf. Manag..

[12]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[13]  Stephen G. MacDonell,et al.  Combining techniques to optimize effort predictions in software project management , 2003, J. Syst. Softw..

[14]  Li-Wei Chen,et al.  Integration of the grey relational analysis with genetic algorithm for software effort estimation , 2008, Eur. J. Oper. Res..

[15]  Chih-Hung Wu,et al.  Developing a business failure prediction model via RST, GRA and CBR , 2009, Expert Syst. Appl..

[16]  David E. Rapach,et al.  Forecasting US employment growth using forecast combining methods , 2008 .

[17]  Emilia Mendes,et al.  A replicated comparison of cross-company and within-company effort estimation models using the ISBSG database , 2005, 11th IEEE International Software Metrics Symposium (METRICS'05).

[18]  Barry W. Boehm,et al.  Software development cost estimation approaches — A survey , 2000, Ann. Softw. Eng..

[19]  Sudip Sanyal,et al.  Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU , 2009, Expert Syst. Appl..

[20]  Giuseppe Parigi,et al.  Energy Consumption, Survey Data and the Prediction of Industrial Production in Italy , 1998 .

[21]  Martin J. Shepperd,et al.  Estimating Software Project Effort Using Analogies , 1997, IEEE Trans. Software Eng..

[22]  Y. Miyazaki,et al.  Robust regression for developing software estimation models , 1994, J. Syst. Softw..

[23]  Kuan-Li Peng,et al.  A Study of Improving the Accuracy of Software Effort Estimation Using Linearly Weighted Combinations , 2010, 2010 IEEE 34th Annual Computer Software and Applications Conference Workshops.

[24]  Michael R. Lyu,et al.  Applying reliability models more effectively (software) , 1992, IEEE Software.

[25]  Sun-Jen Huang,et al.  Optimization of analogy weights by genetic algorithm for software effort estimation , 2006, Inf. Softw. Technol..

[26]  Taghi M. Khoshgoftaar,et al.  An empirical study of predicting software faults with case-based reasoning , 2006, Software Quality Journal.

[27]  JIANPING LI,et al.  Feature Selection via Least Squares Support Feature Machine , 2007, Int. J. Inf. Technol. Decis. Mak..

[28]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[29]  Sungbin Cho,et al.  A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction , 2010, Expert Syst. Appl..

[30]  Sun-Jen Huang,et al.  The adjusted analogy-based software effort estimation based on similarity distances , 2007, J. Syst. Softw..

[31]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[32]  Barry W. Boehm.  Understanding and Controlling Software Costs , 1988 .

[33]  Honggang Wang,et al.  Empirical Evaluation of Classifiers for Software Risk Management , 2009, Int. J. Inf. Technol. Decis. Mak..