C4.5: programs for machine learning
C4.5: programs for machine learning
Characterizing and modeling the cost of rework in a library of reusable software components
ICSE '97 Proceedings of the 19th international conference on Software engineering
Software quality classification model based on McCabe's complexity measure
Journal of Systems and Software - Special issue on achieving quality in software
Evaluating predictive quality models derived from software measures: lessons learned
Journal of Systems and Software
Deriving models of software fault-proneness
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Controlling Overfitting in Classification-Tree Models ofSoftware Quality
Empirical Software Engineering
Predicting Fault-Proneness using OO Metrics: An Industrial Case Study
CSMR '02 Proceedings of the 6th European Conference on Software Maintenance and Reengineering
Detection of software modules with high debug code churn in a very large legacy system
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Classification Tree Models of Software Quality Over Multiple Releases
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
Resource-oriented software quality classification models
Journal of Systems and Software
Comparing Fault-Proneness Estimation Models
ICECCS '05 Proceedings of the 10th IEEE International Conference on Engineering of Complex Computer Systems
Extracting classification rule of software diagnosis using modified MEPA
Expert Systems with Applications: An International Journal
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
IEEE Transactions on Software Engineering
Data Mining Techniques for Building Fault-proneness Models in Telecom Java Software
ISSRE '07 Proceedings of the The 18th IEEE International Symposium on Software Reliability
Combining techniques for software quality classification: An integrated decision network approach
Expert Systems with Applications: An International Journal
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Software classification models have been regarded as an essential support tool in performing measurement and analysis processes. Most of the established models are single-cycled in the model usage stage, and thus require the measurement data of all the model's variables to be simultaneously collected and utilized for classifying an unseen case within only a single decision cycle. Conversely, the multi-cycled model allows the measurement data of all the model's variables to be gradually collected and utilized for such a classification within more than one decision cycle, and thus intuitively seems to have better classification efficiency but poorer classification accuracy. Software project managers often have difficulties in choosing an appropriate classification model that is better suited to their specific environments and needs. However, this important topic is not adequately explored in software measurement and analysis literature. By using an industrial software measurement dataset of NASA KC2, this paper explores the quantitative performance comparisons of the classification accuracy and efficiency of the discriminant analysis (DA)- and logistic regression (LR)-based single-cycled models and the decision tree (DT)-based (C4.5 and ECHAID algorithms) multi-cycled models. The experimental results suggest that the re-appraisal cost of the Type I MR, the software failure cost of Type II MR and the data collection cost of software measurements should be considered simultaneously when choosing an appropriate classification model.