An investigation of artificial neural networks based prediction systems in software project management

A critical issue in software project management is the accurate estimation of size, effort, resources, cost, and time spent in the development process. Underestimates may lead to time pressures that may compromise full functional development and the software testing process. Likewise, overestimates can result in noncompetitive budgets. In this paper, artificial neural network and stepwise regression based predictive models are investigated, aiming at offering alternative methods for those who do not believe in estimation models. The results presented in this paper compare the performance of both methods and indicate that these techniques are competitive with the APF, SLIM, and COCOMO methods.

[1]  D. Ross Jeffery,et al.  Using public domain metrics to estimate software development effort , 2001, Proceedings Seventh International Software Metrics Symposium.

[2]  T. Capers Jones,et al.  Estimating software costs , 1998 .

[3]  Marcelo Francisco Sestini,et al.  MINISTÉRIO DA CIÊNCIA E TECNOLOGIA , 2002 .

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

[5]  A. S. M. Sajeev,et al.  A Vector-Based Approach to Software Size Measurement and Effort Estimation , 2001, IEEE Trans. Software Eng..

[6]  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.

[7]  Richard Lai,et al.  A Model for Estimating the Size of a Formal Communication Protocol Specification and Its Implementation , 2003, IEEE Trans. Software Eng..

[8]  Ingunn Myrtveit,et al.  Reliability and validity in comparative studies of software prediction models , 2005, IEEE Transactions on Software Engineering.

[9]  Taghi M. Khoshgoftaar,et al.  Analyzing software measurement data with clustering techniques , 2004, IEEE Intelligent Systems.

[10]  Ioannis Stamelos,et al.  A Simulation Tool for Efficient Analogy Based Cost Estimation , 2000, Empirical Software Engineering.

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

[12]  Gavin R. Finnie,et al.  Using Artificial Neural Networks and Function Points to Estimate 4GL Software Development Effort , 1994, Australas. J. Inf. Syst..

[13]  Stephen G. MacDonell,et al.  A comparison of techniques for developing predictive models of software metrics , 1997, Inf. Softw. Technol..

[14]  Building a software cost estimation model based on categorical data , 2001, Proceedings Seventh International Software Metrics Symposium.

[15]  David Ellison,et al.  Software cost estimation using an Albus perceptron (CMAC) , 1997, Inf. Softw. Technol..

[16]  Adam A. Porter,et al.  Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis , 1988, IEEE Trans. Software Eng..

[17]  Michael J. Prietula,et al.  Case-Based Reasoning in Software Effort estimation , 1990, International Conference on Interaction Sciences.

[18]  Stephen G. MacDonell,et al.  A comparison of model building techniques to develop predictive equations for software metrics , 1997 .

[19]  José Demisio Simões da Silva,et al.  Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation , 2007, 2007 International Joint Conference on Neural Networks.

[20]  R. Agarwal,et al.  Estimating software projects , 2001, SOEN.

[21]  Douglas Fisher,et al.  Machine Learning Approaches to Estimating Software Development Effort , 1995, IEEE Trans. Software Eng..

[22]  Satish Kumar,et al.  Fuzzy systems and neural networks in software engineering project management , 1994, Applied Intelligence.

[23]  Chris F. Kemerer,et al.  An empirical validation of software cost estimation models , 1987, CACM.

[24]  Ellis Horowitz,et al.  Software Cost Estimation with COCOMO II , 2000 .

[25]  Stephen G. MacDonell,et al.  Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgment , 1999, Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403).

[26]  Jean-Marc Desharnais,et al.  A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models , 1997, J. Syst. Softw..

[27]  Lawrence H. Putnam,et al.  A General Empirical Solution to the Macro Software Sizing and Estimating Problem , 1978, IEEE Transactions on Software Engineering.

[28]  H. E. Dunsmore,et al.  Software engineering metrics and models , 1986 .

[29]  Isabella Wieczorek,et al.  Resource Estimation in Software Engineering , 2002 .

[30]  Barbara A. Kitchenham,et al.  Effort estimation using analogy , 1996, Proceedings of IEEE 18th International Conference on Software Engineering.

[31]  Taghi M. Khoshgoftaar,et al.  Predicting testability of program modules using a neural network , 2000, Proceedings 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology.

[32]  Barbara A. Kitchenham,et al.  A Procedure for Analyzing Unbalanced Datasets , 1998, IEEE Trans. Software Eng..

[33]  Gavin R. Finnie,et al.  Estimating software development effort with connectionist models , 1997, Inf. Softw. Technol..

[34]  L. Darrell Whitley,et al.  Using neural networks in reliability prediction , 1992, IEEE Software.

[35]  John E. Gaffney,et al.  Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation , 1983, IEEE Transactions on Software Engineering.

[36]  S. Gabel,et al.  Using Neural Networks , 2003 .

[37]  Ioannis Stamelos,et al.  Software productivity and effort prediction with ordinal regression , 2005, Inf. Softw. Technol..