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An application of Bayesian network for predicting object-oriented software maintainability
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DOI：10.1016/j.infsof.2005.03.002.png)
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摘要
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As the number of object-oriented software systems increases, it becomes more important for organizations to maintain those systems effectively. However, currently only a small number of maintainability prediction models are available for object-oriented systems. This paper presents a Bayesian network maintainability prediction model for an object-oriented software system. The model is constructed using object-oriented metric data in Li and Henry's datasets, which were collected from two different object-oriented systems. Prediction accuracy of the model is evaluated and compared with commonly used regression-based models. The results suggest that the Bayesian network model can predict maintainability more accurately than the regression-based models for one system, and almost as accurately as the best regression-based model for the other system. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
object-oriented systems
maintainability
Bayesian network
regression tree
regression
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