article

Software development effort prediction: A study on the factors impacting the accuracy of fuzzy logic systems

Authors:

Zeeshan Muzaffar,

Moataz A. AhmedAuthors Info & Claims

Information and Software Technology, Volume 52, Issue 1

Pages 92 - 109

https://doi.org/10.1016/j.infsof.2009.08.001

Published: 01 January 2010 Publication History

Abstract

Reliable effort prediction remains an ongoing challenge to software engineers. Traditional approaches to effort prediction such as the use of models derived from historical data, or the use of expert opinion are plagued with issues pertaining to their effectiveness and robustness. These issues are more pronounced when the effort prediction is used during the early phases of the software development lifecycle. Recent works have demonstrated promising results obtained with the use of fuzzy logic. Fuzzy logic based effort prediction systems can deal better with imprecision, which characterizes the early phases of most software development projects, for example requirements development, whose effort predictors along with their relationships to effort are characterized as being even more imprecise and uncertain than those of later development phases, for example design. Fuzzy logic based prediction systems could produce further better estimates provided that various parameters and factors pertaining to fuzzy logic are carefully set. In this paper, we present an empirical study, which shows that the prediction accuracy of a fuzzy logic based effort prediction system is highly dependent on the system architecture, the corresponding parameters, and the training algorithms.

References

[1]

Ahmed, M.A., Saliu, M.O. and Al-Ghamdi, J., Adaptive fuzzy logic based framework for software development effort prediction. Information and Software Technology. v47 i1. 31-48.

[2]

Ahmed, M.A. and Muzaffar, Z., Handling imprecision and uncertainty in software development effort prediction: a type-2 fuzzy logic based framework. Information and Software Technology. v51 i3. 640-654.

[3]

L. Angelis, I. Stamelos, M. Morisio, Building a software cost estimation model based on categorical data, in: The 7th IEEE International Software Metric Symposium, London, England, 2001.

[4]

S. Bibi, I. Stamelos, L. Aggelis, Bayesian belief networks as a software productivity estimation tool, in: 1st Balkan Conference in Informatics, Thessaloniki, Greece, November 2003.

[5]

B. Boehm, C. Abts, S. Chulani, Software Development Cost Estimation Approaches: A Survey, University of Southern California Centre for Software Engineering, Technical Report, USC-CSE-2000-505, 2000.

[6]

Boehm, B., Clark, B., Horowitz, E., Madachy, R., Shelby, R. and Westland, C., Cost models for future software life cycle processes: COCOMO 2.0. Annals of Software Engineering.

[7]

Boehm, B.W., Software Engineering Economics. 1981. Prentice-Hall, Englewood Cliffs, NJ.

[8]

B. Clark, S. Chulani, B. Boehm, Calibrating the COCOMO II post-architecture model, in: Proceedings of International Conference on Software Engineering, 1998, pp. 477-480.

[9]

S. Devnani-Chulani, Bayesian Analysis of Software Cost and Quality Models, Ph.D. Dissertation, Faculty of Graduate School, University of Southern California, May 1999.

[10]

A.C. Hodgkinson, P.W. Garratt, A NeuroFuzzy cost estimator, in: Proceedings of the 3rd International Conference on Software Engineering and Applications - SAE, 1999, pp. 401-406.

[11]

A. Idri, A. Abran, COCOMO cost model using fuzzy logic, in: The 7th International Conference on Fuzzy Theory and Technology, Atlantic City, New Jersy, March 2000.

[12]

International Software Benchmarking Standards Group (ISBSG), <http://www.isbsg.org/>.

[13]

Jain, and Raj, The Art of Computer Systems Performance Analysis. 1991. John Wiley&Sons Inc.

[14]

Jorgensen, M. and Shepperd, M., A systematic review of software development cost estimation studies. IEEE Transactions on Software Engineering. v33 i1. 33-53.

[15]

C. Kirsopp, M.J. Shepperd, J. Hart, Search heuristics, case-based reasoning and software project effort prediction, in: Genetic and Evolutionary Computing Conference (GECCO 2002), AAAI, New York, 2002.

[16]

Larsen, P.M., Industrial applications of fuzzy logic control. International Journal of Man-Machine Studies. v12 i1. 3-10.

[17]

T. Liang, A. Noore, Multistage software estimation, in: Proceedings of the 35th Southeastern Symposium on System Theory, 16-18 March, 2003, pp. 232-236.

[18]

Mamdani, E.H., Applications of fuzzy algorithms for simple dynamic plant. Proceedings of the IEEE. v121 i12.

[19]

Mendel, J.M., Uncertain Rule-Based Fuzzy Logic Systems. 2001. Prentice-Hall, Upper Saddle River, NJ.

[20]

J.M. Mendel, Q. Liang, Pictorial comparison of type-1 and type-2 fuzzy logic systems, in: Proceedings of IASTED International Conference on Intelligent Systems and Control, Santa Barbara, CA, October 1999.

[21]

Musilek, P., Pedryez, W., Succi, G. and Reformat, M., Software cost estimation with fuzzy models. Applied Computing Review. v8 i2. 24-29.

[22]

S.Z. Muzaffar, Adaptive Fuzzy Logic Based Framework for Handling Imprecision and Uncertainty in Software Development Effort Prediction models, M.S. Thesis, King Fahd University of Petroleum and Minerals, 2006.

[23]

D. Nauck, Data Analysis with Neuro-Fuzzy Methods, Habilitation Thesis, University of Magdeburg, 2000.

[24]

Putnam, L.H., A general empirical solution to the macro software sizing and estimating problem. IEEE Transactions on Software Engineering. v4 i4. 345-361.

[25]

M. Saliu, M. Ahmed, J. AlGhamdi, Towards adaptive soft computing based software effort prediction, in: IEEE Meeting of the Fuzzy Information Processing NAFIPS, vol. 1, 27-30 June, 2004, pp. 16-21.

[26]

M.O. Saliu, M.A. Ahmed, Soft Computing based effort prediction systems - a survey, a chapter, in: E. Damiani, L.C. Jain, M. Madravio (Eds.), Soft Computing in Software Engineering, Springer-Verlag Publisher, July 2004, ISBN 3-540-22030-5.

[27]

M. Saliu, Adaptive Fuzzy Logic Based Framework for Software Development Effort Prediction, Master Thesis, Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, 2003.

[28]

C. Schofield, Non Algorithmic Effort Estimation Techniques, Technical Report, Department of Computing, Bournemouth University, England, TR98-01, March 1998.

[29]

Srinivasan, K. and Fisher, D., Machine learning approaches to estimating software development effort. IEEE Transactions on Software Engineering. v21 i2.

[30]

Walston, C.E. and Felix, C.P., A method of programming measurement and estimation. IBM Systems Journal. v16 i1. 54-73.

[31]

Wang, L., Adaptive Fuzzy System and Control: Design and Stability Analysis. 1994. Prentice-Hall, Inc., Englewood Cliffs, New Jersey.

[32]

Zadeh, L.A., Fuzzy sets. Information and Control. v8. 338-353.

[33]

Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning-1. Information Sciences. v8. 199-249.

[34]

Zimmermann, H., Fuzzy Set Theory and Its Applications. 1996. third ed. Kluwer Academic Publishers.

[35]

F. Zonglian, L. Xihui, f-COCOMO: fuzzy constructive cost model in software engineering, in: Proceedings of IEEE International Conference on Fuzzy Systems (IEEE 1992), pp. 331-337.

Cited By

(2018)Ensemble effort estimation using selection and genetic algorithmsInternational Journal of Computer Applications in Technology10.1504/IJCAT.2018.09406158:1(17-28)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1504/IJCAT.2018.094061
Jodpimai PSophatsathit PLursinsap C(2018)Re-estimating software effort using prior phase efforts and data mining techniquesInnovations in Systems and Software Engineering10.1007/s11334-018-0311-z14:3(209-228)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11334-018-0311-z
(2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.infsof.2017.06.002
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Software development effort prediction: A study on the factors impacting the accuracy of fuzzy logic systems
1. Computing methodologies
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Copyright © Elsevier B.V. © 2009.

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Butterworth-Heinemann

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Publication History

Published: 01 January 2010

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Cited By

(2018)Ensemble effort estimation using selection and genetic algorithmsInternational Journal of Computer Applications in Technology10.1504/IJCAT.2018.09406158:1(17-28)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1504/IJCAT.2018.094061
Jodpimai PSophatsathit PLursinsap C(2018)Re-estimating software effort using prior phase efforts and data mining techniquesInnovations in Systems and Software Engineering10.1007/s11334-018-0311-z14:3(209-228)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11334-018-0311-z
(2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.infsof.2017.06.002
de A. Arajo ROliveira AMeira S(2017)A class of hybrid multilayer perceptrons for software development effort estimation problemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.07.05090:C(1-12)Online publication date: 30-Dec-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.07.050

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