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doi:10.1016/j.infsof.2005.03.002 
Copyright © 2005 Elsevier B.V. All rights reserved.
An application of Bayesian network for predicting object-oriented software maintainability
Received 3 October 2004;
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Abstract
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.
Keywords: Object-oriented systems; Maintainability; Bayesian network; Regression tree; Regression
Article Outline
- 1. Introduction
- 2. OO software datasets
- 3. Bayesian network model
- 3.1. Bayesian network
- 3.2. Model construction
- 4. Regression-based models
- 5. Prediction accuracy measures
- 6. Model evaluation and comparison
- 6.1. Results from UIMS dataset
- 6.2. Results from QUES dataset
- 6.3. Discussion
- 7. Conclusions
- Acknowledgements
- References
