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Challenges in Bridging Social Semantics and Formal Semantics on the Web Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System

Authors : Lianfa Li, Hareton Leung

Published in: Enterprise Information Systems

Publisher: Springer International Publishing

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Abstract

Logistic regression (LR) and naïve Bayes (NB) extensively used for prediction of fault-proneness assume linear addition and independence that often cannot hold in practice. Hence, we propose a Bayesian network (BN) model with incorporation of data mining techniques as an integrative approach. Compared with LR and NB, BN provides a flexible modeling framework, thus avoiding the corresponding assumptions. Using the static metrics such as Chidamber and Kemerer’s (C-K) suite and complexity as predictors, the differences in performance between LR, NB and BN models were examined for fault-proneness prediction at the class level in continual releases (five versions) of Rhino, an open-source implementation of JavaScript, developed using the agile process. By cross validation and independent test of continual versions, we conclude that the proposed BN can achieve a better prediction than LR and NB for the agile software due to its flexible modeling framework and incorporation of multiple sophisticated learning algorithms.

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Literature
1.
D’Ambros, M., Lanza, M., Robbes, R.: Evaluating defect prediction approaches: a benchmark and an extensive comparison. Empir. Softw. Eng. 17, 531–577 (2012)CrossRef
2.
Pai, G.J., Dugan, J.B.: Empirical analysis of software fault content and fault proneness using Bayesian methods. IEEE Trans. Software Eng. 33, 675–686 (2007)CrossRef
3.
Menzies, T., Greenwald, J., Frank, A.: Data mining static code attributes to learn defect predictors. IEEE Trans. Software Eng. 33, 2–13 (2007)CrossRef
4.
Briand, L.C., Wust, J., Daly, J.W., Porter, D.V.: Exploring the relationships between design measures and software quality in object-oriented systems. J. Syst. Softw. 51, 245–273 (2000)CrossRef
5.
Singh Y., Kaur, A., Malhotra, R.: Application of decision trees for predicting fault proneness. In: International Conference on Information Systems, Technology and Management-Information Technology, Ghaziabad, India (2009)
6.
Guo, L., Ma, Y., Cukic, B., Singh, H.: Robust prediction of fault proneness by random forests. In: 15th International Symposium on Software Reliability Engineering, pp. 417–428. IEEE Computer Society, Washington, DC (2004)
7.
Singh, Y., Kaur, A., Malhotra, R.: Predicting software fault proneness model using neural network. In: Jedlitschka, A., Salo, O. (eds.) PROFES 2008. LNCS, vol. 5089, pp. 204–214. Springer, Heidelberg (2008)CrossRef
8.
Singh, Y., Kaur, A., Malhotra, R.: Software fault proneness prediction using support vector machines. In: Proceedings of the World Congress on Engineering (2009)
9.
Hosmer, D., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (2000)
10.
Gokhale, S.S., Lyn, M.R.: Regression tree modeling for the prediction of software quality. In: Proceedings Of Third ISSAT Intl. Conference on Reliability, pp. 31–36 (1997)
11.
John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–346 (1995)
12.
Ambler, S.W.: Agile Modeling: Effective Practices for Extreme Programming and the Unified Process. Wiley, New York (2002)
13.
Herbsleb, J.D.: Global software development. IEEE Softw. 18, 16–20 (2001)CrossRef
14.
15.
Olague, H.M., Etzkorn, L.H., Gholston, S., Quattlebaum, S.: Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. IEEE Trans. Softw. Eng. 33, 402–419 (2007)CrossRef
16.
Basili, V.R., Briand, L.C., Melo, W.L.: A validation of object-oriented design metrics as quality indicators. IEEE Trans. Softw. Eng. 22, 751–761 (1996)CrossRef
17.
Cardoso, J.: Process control-flow complexity metric: an empirical validation. In: IEEE International Conference on Services Computing (IEEE SCC 06), pp. 167–173. IEEE Computer Society (2006)
18.
Harrison, R., Counsell, S., Nithi, R.: An evaluation of the MOOD set of object oriented software metrics. IEEE Trans. Softw. Eng. 24, 150–157 (1998)CrossRef
19.
20.
21.
22.
Spinellis, D.: Code Quality: The Open Source Perspective. Addison Wesley, Boston (2006)
23.
24.
Elomaa, T., Rousu, J.: Finding optimal multi-splits for numerical attributes in decision tree learning (1996)
25.
Li, L., Wang, J., Leung, H., Jiang, C.: Assessment of catastrophic risk using bayesian network constructed from domain knowledge and spatial data. Risk Anal. 30, 1157–1175 (2010)CrossRef
26.
Bouckaert, R.R.: Bayesian Belief Network: from Construction to Inference (1995)
27.
Kabli, R., Herrmann, F., McCall, J.: A Chain-Model Genetic Algorithm for Bayesian Network Structure Learning. GECCO, London (2007)
28.
Larranaga, P., Murga, R., Poza, M., Kuijpers, C.: Structure learning of Bayesian network by hybrid genetic algorithms. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data: AI and Statistics. Springer, New York (1996)
29.
Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence. Chapman & Hall/CRC, Boca Raton (2004)
30.
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kauffman, San Francisco (1993)
31.
Dirk, V.P., Bart, L.: Customer attrition analysis for financial services using proportional hazard models. Eur. J. Oper. Res. 157, 196–217 (2004)CrossRefMATH
32.
33.
Menzies, T., Dekhtyar, A., Distefano, J., Greenwald, J.: Problems with precision: a response to “comments on ‘data mining static code attributes to learn defect predictors’”. IEEE Trans. Softw. Eng. 33, 637–640 (2007)CrossRef
34.
Liu, Y., Cheah, W., Kim, B., Park, H.: Predict software failure-prone by learning Bayesian network. Int. J. Adv. Sci. Technol. 1, 33–42 (2008)
35.
Fenton, N., Neil, M., Marsh, W., Hearty, P., Radlinski, L., Krause, P.: On the effectiveness of early life cycle defect prediction with Bayesian nets. Empir. Softw. Eng. 13, 499–537 (2008)CrossRef
36.
Li, L., Leung, H.: Mining static code metrics for a robust prediction of software defect-proneness. In: ACM /IEEE International Symposium on Empirical Software Engineering and Measurement Anaheim, CA (2011)
37.
Cox, A.L.: Risk Analysis: Foundations, Models and Methods. Springer, Heidelberg (2001)
38.
39.
Li, L., Leung, H.: Using the number of faults to improve fault-proneness prediction of the probability models. In: 2009 World Congress on Computer Science and Information Engineering, Los Angeles/Anaheim (2009)
Metadata
Title
Bayesian Prediction of Fault-Proneness of Agile-Developed Object-Oriented System
Authors
Lianfa Li
Hareton Leung
Copyright Year
2014
Book
Enterprise Information Systems

Print ISBN: 978-3-319-09491-5

Electronic ISBN: 978-3-319-09492-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-09492-2_13

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