A comparison of software effort prediction models using small datasets
University of Otago, 2011
Online
unknown
Zugriff:
Submitted to IEEE Transactions on Software Engineering. If published, this version will be replaced by the final version. ; Constructing an accurate effort prediction model is a challenge in Software Engineering. One difficulty practitioners often experience is that they have only a very small amount of local data to construct a model. The small dataset limits predictive accuracy of the model, since the accuracy deteriorates as the size of the dataset decreases. This paper compares three different software development effort prediction models that are applicable to these small datasets. They are: (1) Bayesian statistical models, (2) multiple linear regression models and (3) case-based reasoning/analogy-based models. The predictive accuracy of these models is evaluated using two different software datasets. The results have shown that the accuracy of the Bayesian statistical models is higher than or competitive with that of the others, when calibrated using data collected from fewer than 10 systems. These suggest that the Bayesian statistical model would be a better choice in effort prediction when the practitioners have only a very small dataset, consisting of fewer than 10 systems similar to their system of interest. ; Submitted ; [1] C.J. Burgess and M. Lefley. Can genetic programming improve software effort estimation? a comparative evaluation. Information and Software Technology, 43:863–873, 2001. [2] P. Congdon. Bayesian Statistical Modelling. John Wiley & Sons., 2001. [3] S.D. Conte, H.E. Dunsmore, and V.Y. Shen. Software Engineering Metrics and Models. Benjamin/Cummings Publishing Company, 1986. [4] N.E. Fenton and S.L. Pfleeger. Software Metrics:A Rigorous & Practical Approach. PWS Publishing Company, second edition, 1997. [5] T. Foss, E. Stensrud, B. Kitchenham, and I. Myrtveit. A simulation study of the model evaluation criterion mmre. IEEE Transactions on Software Engineering, 29(11):985–995, 2003. [6] R.L. Glass. Frequently forgotten fundamental facts about software engineering. IEEE ...
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A comparison of software effort prediction models using small datasets
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Autor/in / Beteiligte Person: | van Koten, Chikako |
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Veröffentlichung: | University of Otago, 2011 |
Medientyp: | unknown |
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