Computational intelligence in software cost estimation: an emerging paradigm

One of the key features for the failure of project estimation techniques is the selection of inappropriate estimation models. Further, noisy data poses a challenge to build accurate estimation models. Therefore, the software cost estimation (SCE) is a challenging problem that has attracted many researchers over the past few decades. In the recent times,the use of computational intelligence methodologies for software cost estimation have gained prominence. This paper reviews some of the commonly used computational intelligence (CI) techniques and analyzes their application in software cost estimation and outlines the emerging trends in this area

[1]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[2]  Zeeshan Muzaffar,et al.  Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework , 2009, Inf. Softw. Technol..

[3]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[4]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[5]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[6]  Danny Ho,et al.  Improving the COCOMO model using a neuro-fuzzy approach , 2007, Appl. Soft Comput..

[7]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[8]  Lawrence O. Hall,et al.  Joint 9th IFSA World Congress and 20th NAFIPS International Conference, July 25-28, 2001, Vancouver, British Columbia, Canada, Coast Plaza Suite Hotel at Stanley Park , 2001 .

[9]  Magne Jørgensen,et al.  A review of studies on expert estimation of software development effort , 2004, J. Syst. Softw..

[10]  Liang Tian,et al.  Multistage software estimation , 2003, Proceedings of the 35th Southeastern Symposium on System Theory, 2003..

[11]  Barbara A. Kitchenham,et al.  Using simulated data sets to compare data analysis techniques used for software cost modelling , 2001, IEE Proc. Softw..

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

[13]  Amrit L. Goel,et al.  Empirical Data Modeling in Software Engineering Using Radical Basis Functions , 2000, IEEE Trans. Software Eng..

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

[15]  Barry W. Boehm,et al.  Software Engineering Economics , 1993, IEEE Transactions on Software Engineering.

[16]  Mario Jino,et al.  Machine Learning Methods and Asymmetric Cost Function to Estimate Execution Effort of Software Testing , 2010, 2010 Third International Conference on Software Testing, Verification and Validation.

[17]  Lotfi A. Zadeh,et al.  Generalized theory of uncertainty (GTU) - principal concepts and ideas , 2006, Comput. Stat. Data Anal..

[18]  Taghi M. Khoshgoftaar,et al.  Estimating software project effort by analogy based on linguistic values , 2002, Proceedings Eighth IEEE Symposium on Software Metrics.

[19]  Yew-Soon Ong,et al.  Memetic Computation—Past, Present & Future [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[20]  Lotfi A. Zadeh,et al.  Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems , 1998, Soft Comput..

[21]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[22]  Tian Peng,et al.  On Social Technology , 2005 .

[23]  Thong Ngee Goh,et al.  A study of project selection and feature weighting for analogy based software cost estimation , 2009, J. Syst. Softw..

[24]  John R. Koza,et al.  Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems , 1990 .

[25]  Adriano Lorena Inácio de Oliveira,et al.  Estimation of software project effort with support vector regression , 2006, Neurocomputing.

[26]  Stefan Biffl,et al.  Optimal project feature weights in analogy-based cost estimation: improvement and limitations , 2006, IEEE Transactions on Software Engineering.

[27]  Lionel C. Briand,et al.  A replicated assessment and comparison of common software cost modeling techniques , 2000, Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium.

[28]  Donald J. Reifer.  Software management , 1980, AFIPS '80.

[29]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[30]  Abbas Heiat,et al.  Comparison of artificial neural network and regression models for estimating software development effort , 2002, Inf. Softw. Technol..

[31]  Martin Shepperd,et al.  Experiences Using Case-Based Reasoning to Predict Software Project Effort , 2000 .

[32]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[33]  Raymond Madachy,et al.  A software project dynamics model for process cost, schedule and risk assessment , 1994 .

[34]  Robert Tausworthe,et al.  The work breakdown structure in software project management , 1984, J. Syst. Softw..

[35]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[36]  Magne Jørgensen.  Top-down and bottom-up expert estimation of software development effort , 2004, Inf. Softw. Technol..

[37]  Emilia Mendes,et al.  Investigating Web size metrics for early Web cost estimation , 2005, J. Syst. Softw..

[38]  Ware Myers,et al.  Five Core Metrics: Intelligence behind Successful Software Management , 2003 .

[39]  Jun Zhang,et al.  Evolutionary Computation Meets Machine Learning: A Survey , 2011, IEEE Computational Intelligence Magazine.

[40]  Harinder P. Singh,et al.  Fuzzy Sets and Information , 2010 .

[41]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[42]  M. Ahmed,et al.  Towards adaptive soft computing based software effort prediction , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[43]  W. Eric L. Grimson,et al.  Prototype optimization for nearest-neighbor classification , 2002, Pattern Recognit..

[44]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[45]  Manfred Bundschuh,et al.  The IT measurement compendium - estimating and benchmarking success with functional size measurement , 2008 .

[46]  S S Stevens,et al.  On the Theory of Scales of Measurement. , 1946, Science.

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

[48]  Lionel C. Briand,et al.  An assessment and comparison of common software cost estimation modeling techniques , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[49]  Martin J. Shepperd,et al.  Comparing Software Prediction Techniques Using Simulation , 2001, IEEE Trans. Software Eng..

[50]  Genny Tortora,et al.  Class point: an approach for the size estimation of object-oriented systems , 2005, IEEE Transactions on Software Engineering.

[51]  Iman Attarzadeh,et al.  Improving estimation accuracy of the COCOMO II using an adaptive fuzzy logic model , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

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

[53]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[54]  Moataz A. Ahmed,et al.  Adaptive fuzzy logic-based framework for software development effort prediction , 2005, Inf. Softw. Technol..