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Title: Predicting fault-proneness of object-oriented system developed with agile process using learned Bayesian network
Authors: Li, L
Leung, HKN 
Keywords: Object-Oriented systems
Software quality
Data mining
Issue Date: 2013
Source: 15th International Conference on Enterprise Information Systems (ICEIS 2013), Angers, France, July 4-7, 2013, p. 5-16 How to cite?
Abstract: In the prediction of fault-proneness in object-oriented (OO) systems, it is essential to have a good prediction method and a set of informative predictive factors. Although logistic regression (LR) and naïve Bayes (NB) have been used successfully for prediction of fault-proneness, they have some shortcomings. In this paper, we proposed the Bayesian network (BN) with data mining techniques as a predictive model. Based on the Chidamber and Kemerer’s (C-K) metric suite and the cyclomatic complexity metrics, we examine the difference in the performance of LR, NB and BN models for the fault-proneness prediction at the class level in continual releases (five versions) of Rhino, an open-source implementation of JavaScript written in Java. From the viewpoint of modern software development, Rhino uses a highly iterative or agile development methodology. Our study demonstrates that the proposed BN can achieve a better prediction than LR and NB for the agile software.
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