Information Retrieval
Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Comparison of Bug Finding Tools for Java
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Combining winnow and orthogonal sparse bigrams for incremental spam filtering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
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
Review: A systematic review of software fault prediction studies
Expert Systems with Applications: An International Journal
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We have proposed a detection method of fault-pronemodules based on the spam filtering technique, "Fault-prone filtering." Faultprone filtering is a method which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer are a good source of fault-prone filtering as the original source code. Moreover, it is discovered that it is more effective than the conventional method (that is, without static code analyzer) to raise the coverage rate of actual faulty modules.