Bayesian statistical models for predicting software development effort
In: Information Science Discussion Papers Series; 22; (2011)
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Zugriff:
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper presents new Bayesian statistical models, in order to predict development effort of software systems in the International Software Benchmarking Standards Group (ISBSG) dataset. The first model is a Bayesian linear regression (BR) model and the second model is a Bayesian multivariate normal distribution (BMVN) model. Both models are calibrated using subsets randomly sampled from the dataset. The models’ predictive accuracy is evaluated using other subsets, which consist of only the cases unknown to the models. The predictive accuracy is measured in terms of the absolute residuals and magnitude of relative error. They are compared with the corresponding linear regression models. The results show that the Bayesian models have predictive accuracy equivalent to the linear regression models, in general. However, the advantage of the Bayesian statistical models is that they do not require a calibration subset as large as the regression counterpart. In the case of the ISBSG dataset it is confirmed that the predictive accuracy of the Bayesian statistical models, in particular the BMVN model is significantly better than the linear regression model, when the calibration subset consists of only five or smaller number of software systems. This finding justifies the use of Bayesian statistical models in software effort prediction, in particular, when the system of interest has only a very small amount of historical data. ; Unpublished ; C.G. Bai. Bayesian network based software reliability prediction with an operational profile. The Journal of Systems and Software, 77:103–112, 2005. C.G. Bai, Q.P. Hu, M. Xie, and S.H. Ng. Software failure prediction based on a Markov bayesian network model. The Journal of Systems and Software, 74:275–282, 2005. J. Baik, B. Boehm, and B.M. Steece. Disaggregating and calibrating the CASE tool variable in COCOMO II. IEEE Transactions on Software Engineering, 28(11):1009–1022, 2002. S. Chulani, B. Boehm, and B.M. Steece. Bayesian analysis of empirical software engineering cost models. IEEE Transactions on Software Engineering, 25(4):513–583, 1999. P. Congdon. Bayesian Statistical Modelling. John Wiley & Sons., 2001. S.D. Conte, H.E. Dunsmore, and V.Y. Shen. Software Engineering Metrics and Models. Benjamin/Cummings Publishing Company, 1986. C. Fan and Y. Yu. BBN-based software project risk management. The Journal of Systems and Software, 73:193–203, 2004. N. Fenton and M. Neil. A critique of software defect prediction models. IEEE Transactions on Software Engineering, 25(5):675–689, 1999. N.E. Fenton and S.L. Pfleeger. Software Metrics:A Rigorous & Practical Approach. PWS Publishing Company, second edition, 1997. 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. P.J. Green. A primer on markov chain monte carlo. In O.E. Barndorff-Nielsen, D.R. Cox, and C. Klüppelberg, editors, Complex Stochastic Systems, chapter 1, pages 1–62. Chapman & Hall/CRC, 2001. F.V. Jensen. Bayesian Networks and Decision Graphs. Springer–Verlag New York, 2001. B.A. Kitchenham, L.M. Pickard, S.G. MacDonell, and M.J. Shepperd. What accuracy statistics really measure. IEE Proceedings–Software, 148(3):81–85, 2001. S.G. MacDonell. Establishing relationships between specification size and software process effort in case environment. Information and Software Technology, 39:35–45, 1997. J. Moses. Bayesian probability distributions for assessing measurement of subjective software attributes. Information and Software Technology, 42:533–546, 2000. M. Neil, N. Fenton, and L. Nielsen. Building large-scale bayesian networks. The Knowledge Engineering Review, 15(3):257–284, 2000. P.C. Pendharkar, G.H. Subramanian, and J.A. Rodger. A probabilistic model for predicting software development effort. IEEE Transactions on Software Engineering, 31(7):615–624, 2005. I. Stamelos, L. Angelis, P. Dimou, and E. Sakellaris. On the use of Bayesian belief networks for the prediction of software productivity. Information and Software Technology, 45:51–60, 2003. E. Stensrud, T. Foss, B.A. Kitchenham, and I. Myrtveit. An empirical validation of the relationship between the magnitude of relative error and project size. In Proceedings of the 8th IEEE Symposium on Software Metrics (METRICS’02), pages 3–12, 2002. B. Stewart. Predicting project delivery rates using the Naive–Bayes classifier. Journal of Software Maintenance and Evolution: Research and Practice, 14:161–179, 2002. C. van Koten and A.R. Gray. An application of bayesian network for predicting object-oriented software maintainability. Information and Software Technology, in press, 2005. D.A. Wooff, M. Goldstein, and F.P.A. Coolen. Bayesian graphical models for software testing. IEEE Transactions on Software Engineering, 28(5):510–525, 2002.
Titel: |
Bayesian statistical models for predicting software development effort
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Autor/in / Beteiligte Person: | van Koten, Chikako |
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Quelle: | Information Science Discussion Papers Series; 22; (2011) |
Veröffentlichung: | University of Otago, 2011 |
Medientyp: | report |
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