Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Testing Very Big Systems
An empirical evaluation of fault-proneness models
Proceedings of the 24th International Conference on Software Engineering
Controlling Overfitting in Classification-Tree Models ofSoftware Quality
Empirical Software Engineering
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
Software Quality Classification Modeling Using The SPRINT Decision Tree Algorithm
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
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
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
Comparing Fault-Proneness Estimation Models
ICECCS '05 Proceedings of the 10th IEEE International Conference on Engineering of Complex Computer Systems
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Analyzing Software Quality with Limited Fault-Proneness Defect Data
HASE '05 Proceedings of the Ninth IEEE International Symposium on High-Assurance Systems Engineering
Proceedings of the 2006 international workshop on Mining software repositories
International Conference on Software Engineering
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions 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
Hackers & Painters: Big Ideas from the Computer Age
Hackers & Painters: Big Ideas from the Computer Age
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This paper describes a novel approach for detecting fault-prone modules using a spam filtering technique. Fault-prone module detection in source code is important for the assurance of software quality. Most previous fault-prone detection approaches have been based on using software metrics. Such approaches, however, have difficulties in collecting the metrics and constructing mathematical models based on the metrics. Because of the increase in the need for spam e-mail detection, the spam filtering technique has progressed as a convenient and effective technique for text mining. In our approach, fault-prone modules are detected in such a way that the source code modules are considered text files and are applied to the spam filter directly. 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 correctly predict 78% of actual fault-prone modules as fault-prone.