Stefan Florea - Academia.edu (original) (raw)
Address: Hoboken, New Jersey, United States
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As the use and the complexity of the object-oriented software systems increase, it becomes necess... more As the use and the complexity of the object-oriented software systems increase, it becomes necessary for organizations to maintain those systems in a cost-effective way. Maintainability, is the quality attributes that indicates the ease with which a software system can be modified, can be debugged or can conform to changing requirements. Quantifying maintainability is necessary from the early phases of development, because it provides useful information to improve the design, the code and the overall software quality. In order to quantify maintainability, several maintainability prediction models have been introduced the last twenty years. Our research will be focused on two recent maintainability prediction models, which have been characterized as the most accurate, by an independent study. The first is based on Bayesian networks and the second on multivariate adaptive regression splines (MARS). Both models are constructed using the metrics and dataset proposed by Li and Henry. In this study, we compare the above-mentioned models, with respect to their techniques, the calculated metrics and the produced results. The results show that the prediction accuracy of the models vary depending on the characteristics of the data sets.
As the use and the complexity of the object-oriented software systems increase, it becomes necess... more As the use and the complexity of the object-oriented software systems increase, it becomes necessary for organizations to maintain those systems in a cost-effective way. Maintainability, is the quality attributes that indicates the ease with which a software system can be modified, can be debugged or can conform to changing requirements. Quantifying maintainability is necessary from the early phases of development, because it provides useful information to improve the design, the code and the overall software quality. In order to quantify maintainability, several maintainability prediction models have been introduced the last twenty years. Our research will be focused on two recent maintainability prediction models, which have been characterized as the most accurate, by an independent study. The first is based on Bayesian networks and the second on multivariate adaptive regression splines (MARS). Both models are constructed using the metrics and dataset proposed by Li and Henry. In this study, we compare the above-mentioned models, with respect to their techniques, the calculated metrics and the produced results. The results show that the prediction accuracy of the models vary depending on the characteristics of the data sets.