A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
An empirical evaluation of fault-proneness models
Proceedings of the 24th International Conference on Software Engineering
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Further Comparison of Cross-Company and Within-Company Effort Estimation Models for Web Applications
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Looking for bugs in all the right places
Proceedings of the 2006 international symposium on Software testing and analysis
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Cross versus Within-Company Cost Estimation Studies: A Systematic Review
IEEE Transactions on Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Comparing Local and Global Software Effort Estimation Models -- Reflections on a Systematic Review
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Application of support vector machine to predict fault prone classes
ACM SIGSOFT Software Engineering Notes
Towards identifying software project clusters with regard to defect prediction
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
A genetic algorithm to configure support vector machines for predicting fault-prone components
PROFES'11 Proceedings of the 12th international conference on Product-focused software process improvement
An investigation on the feasibility of cross-project defect prediction
Automated Software Engineering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Training data selection for cross-project defect prediction
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
CMS tool: calculating defect and change data from software project repositories
ACM SIGSOFT Software Engineering Notes
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An important step in predicting error prone modules in a project is to construct the prediction model by using training data of that project, but the resulting prediction model depends on the training data. Therefore it is difficult to apply the model to other projects. The training data consists of metrics data and bug data, and these data should be prepared for each project. Metrics data can be computed by using metric tools, but it is not so easy to collect bug data. In this paper, we try to reuse the generated prediction model. By using the metrics and bug data which are computed from C++ and Java projects, we have evaluated the possibility of applying the prediction model, which is generated based on one project, to other projects, and have proposed compensation techniques for applying to other projects. We showed the evaluation result based on open source projects.