Functional networks as a novel data mining paradigm in forecasting software development efforts

Expert Systems with Applications

Volumn 38, Issue 3, 2011, Pages 2187-2194

  El Sebakhy, Emad A ^a  

^a ELSEVIER MASSON   (France)

Author keywords

Data mining; Functional networks; Neural networks; Regression; Software development effort; Support vector machines; Type I fuzzy logic inference system

Indexed keywords

FUNCTIONAL NETWORK; REGRESSION; SOFTWARE DEVELOPMENT EFFORT; SUPPORT VECTOR; TYPE I FUZZY LOGIC INFERENCE SYSTEM;

CASE BASED REASONING; COMPUTER SOFTWARE; DATA MINING; FORECASTING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

SOFTWARE DESIGN;

EID: 78049526683     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.08.005     Document Type: Article

References (27)

1. M.A. Ahmed, M.O. Saliu, and J. AlGhamdi Adaptive fuzzy logic-based framework for software development effort prediction Information and Software Technology 47 1 2005 31 48
2. A.J. Albrecht, and J. Gaffney Software function, source lines of code, and development effort prediction: A software science validation IEEE Transactions on Software Engineering SE-9 6 1983 639 648
3. A.J. Albrecht, and J. Gaffney Software function, source lines of code, and development effort prediction IEEE Transactions on Software Engineering 9 6 1983 639 648
4. Boehm's HYPERLINK Data (1981).
5. E. Castillo Functional networks Neural Processing Letters 7 3 1998 151 159
6. E. Castillo, A. Cobo, J.M. Gutierrez, and A.S. Hadi A general framework for functional networks Networks 35 1999 70 82
7. El-Sebakhy, A. E. (2004). Functional networks training algorithm for statistical pattern recognition. In Ninth international symposium on IEEE symposium on computers and communications, ISCC 2004, 28 June-1 July (Vol. 1, pp. 92 -97).
8. E.A. El-Sebakhy Software reliability identification using functional networks: A comparative study International Journal of Expert Systems with Application August 2008 4013 4020
9. El-Sebakhy, E.; Faisal, K.; El-Bassuny, T.; Azzedin, F.; & Al-Suhaim, A. (2006). Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. In The 4th ACS/IEEE international conference on computer systems and applications (pp. 281-287).
10. A.E. El-Sebakhy, S.A. Hadi, and A.K. Faisal Iterative least squares functional networks classifier IEEE Transactions Neural Networks 18 3 2007 844 850
11. G.R. Finnie, and G.E. Wittig Artificial intelligence tools for software development effort estimation International conference on software engineering: Education and practice 1996 IEEE Computer Society Washington 346
12. A. Heiat Comparison of artificial neural network and regression models for estimating software development effort Information and Software Technology 44 15 2002 911 922
13. X. Huang, D. Ho, J. Ren, and L.F. Capretz Improving the COCOMO model using a neuro-fuzzy approach Applied Soft Computing 7 1 2007 29 40
14. E.S. Jun, and J.K. Lee Quasi-optimal case-selective neural network model for software effort estimation Expert Systems with Applications 21 1 2001 1 14
15. Kadoda, G.; Cartwright, M.; & Shepperd, M. (2000a). On configuring a case-based reasoning software project prediction system.
16. Kadoda, G.; Cartwright, M.; Chen, L.; & Shepperd, M. (2000b). Experiences using casebased reasoning to predict software project effort. In Proceedings of the 4th international conference on empirical assessment and evaluation in software engineering. Staffordshire, UK: Keele University.
17. C.F. Kemerer An empirical validation of software cost estimation models Communications of the ACM 30 1987 416 429
18. C.F. Kemerer Software cost estimation models Software engineers reference handbook 1991 Butterworth Surrey, UK (Chapter 25)
19. H. Leung, and Z. Fan Software cost estimation 2002 Handbook of Software Engineering
20. Martin, C. L.; Pasquier, J. L.; Yanez, C. M.; & Tornes, A. G. (2005). Software development effort estimation using fuzzy logic: A case study. Computer science (pp. 113-120). Mexico: IEEE.
21. H. Park, and S. Baek An empirical validation of a neural network model for software effort estimation 2007 Elsevier
22. P.C. Pendharkar, G.H. Subramanian, and J.A. Rodger A probabilistic model for predicting software development effort IEEE Transactions on Software Engineering (IEEE Press) 31 7 2005 615 624
23. Quah, T.; & Thwin, M. (2003). Application of neural networks for software quality prediction using object-oriented metrics. In Proceedings of the international conference on software maintenance (ICSM'03) (pp. 116-125).
24. Ryder, J. (1998). Fuzzy modeling of software effort prediction. In IT conference (pp. 53-56). IEEE.
25. I.F. De Barcelos Tronto, J.D. Simíes de Silva, and N. Sant'Anna An investigation of artificial neural networks based prediction systems in software project management Journal of Systems and Software 81 3 2008 356 367
26. C. van Koten, and A.R. Gray Bayesian statistical effort prediction models for data-centred 4GL software development Information and Software Technology 48 11 2006 1056 1067
27. Y. Zhou, and H. Leung Predicting object-oriented software maintainability using multivariate adaptive regression splines Journal of Systems and Software 80 8 2007 1349 1361