A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
An Enhanced Neural Network Technique for Software Risk Analysis
IEEE Transactions on Software Engineering
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Empirical Assessment of Machine Learning based Software Defect Prediction Techniques
WORDS '05 Proceedings of the 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems
Building Defect Prediction Models in Practice
IEEE Software
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Database for the Analysis of Program Change Patterns
NCM '08 Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 02
Empirical Evaluation of Hunk Metrics as Bug Predictors
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Towards a software failure cost impact model for the customer: an analysis of an open source product
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
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Open Source Softwares provide a rich resource of empirical research in software engineering. Static code metrics are a good indicator of software quality and maintainability. In this work we have tried to answer the question whether bug predictors obtained from one project can be applied to a different project with reasonable accuracy. Two open source projects Firefox and Apache HTTP Server (AHS) are used for this study. Static code metrics are calculated for both projects using in-house software and the bug information is obtained from bug databases of these projects. The source code files are classified as clean or buggy using the Decision tree classifier. The classifier is trained on metrics and bug data of Firefox and tested on Apache HTTP Server and vice versa. The results obtained vary with different releases of these projects and can be as good as 92 % of the files correctly classified and as poor as 68 % of the files correctly classified by the trained classifier.