The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Proceedings of the 2006 international workshop on Mining software repositories
International Conference on Software Engineering
Training on errors experiment to detect fault-prone software modules by spam filter
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
An extension of fault-prone filtering using precise training and a dynamic threshold
Proceedings of the 2008 international working conference on Mining software repositories
Mining software repositories for software change impact analysis: a case study
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
Fault-prone module detection using large-scale text features based on spam filtering
Empirical Software Engineering
Creating Process-Agents incrementally by mining process asset library
Information Sciences: an International Journal
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Because of the increase of needs for spam e-mail detection, the spam filtering technique has been improved as a convenient and effective technique for text mining. We propose a novel approach to detect fault-prone modules in a way that the source code modules are considered as text files and are applied to the spam filter directly. In order to show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can classify more than 75% of software modules correctly.