Applied multivariate techniques
Applied multivariate techniques
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis
IEEE Transactions on Software Engineering
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
Object-oriented metrics: A review of theory and practice
Advances in software engineering
Computer-Aided Multivariate Analysis
Computer-Aided Multivariate Analysis
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
Data Mining Using SAS Applications
Data Mining Using SAS Applications
Exploratory study of a UML metric for fault prediction
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
Software fault prediction for object oriented systems: a literature review
ACM SIGSOFT Software Engineering Notes
Empirical evaluation of the effects of mixed project data on learning defect predictors
Information and Software Technology
Training data selection for cross-project defect prediction
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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In this paper, we discuss the challenge of making logistic regression models able to predict fault-prone object-oriented classes across software projects. Several studies have obtained successful results in using design-complexity metrics for such a purpose. However, our data exploration indicates that the distribution of these metrics varies from project to project, making the task of predicting across projects difficult to achieve. As a first attempt to solve this problem, we employed simple log transformations for making design-complexity measures more comparable among projects. We found these transformations useful in projects which data is not as spread as the data used for building the prediction model.