Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
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
CCFinder: a multilinguistic token-based code clone detection system for large scale source code
IEEE Transactions on Software Engineering
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
Predicting Fault-Prone Software Modules in Embedded Systems with Classification Trees
HASE '99 The 4th IEEE International Symposium on High-Assurance Systems 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
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
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Spam Filter Based Approach for Finding Fault-Prone Software Modules
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
An extension of fault-prone filtering using precise training and a dynamic threshold
Proceedings of the 2008 international working conference on Mining software repositories
Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator
IEICE - Transactions on Information and Systems
Fault-prone module detection using large-scale text features based on spam filtering
Empirical Software Engineering
An integrated approach to detect fault-prone modules using complexity and text feature metrics
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Do comments explain codes adequately?: investigation by text filtering
Proceedings of the 8th Working Conference on Mining Software Repositories
Can faulty modules be predicted by warning messages of static code analyzer?
Advances in Software Engineering - Special issue on Software Quality Assurance Methodologies and Techniques
Bug prediction based on fine-grained module histories
Proceedings of the 34th International Conference on Software Engineering
Predicting method crashes with bytecode operations
Proceedings of the 6th India Software Engineering Conference
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The fault-prone module detection in source code is of importance for assurance of software quality. Most of previous fault-prone detection approaches are based on software metrics. Such approaches, however, have difficulties in collecting the metrics and constructing mathematical models based on the metrics. In order to mitigate such difficulties, we propose a novel approach for detecting fault-prone modules using a spam filtering technique, named Fault-Prone Filtering. Because of the increase of needs for spam e-mail detection, the spam filtering technique has been progressed as a convenient and effective technique for text mining. In our approach, fault-prone modules are detected in a way that the source code modules are considered as text files and are applied to the spam filter directly. This paper describes the training on errors procedure to apply fault-prone filtering in practice. Since no pre-training is required, this procedure can be applied to actual development field immediately. In order to show the usefulness of our approach, we conducted an experiment using a large source code repository of Java based open source project. The result of experiment shows that our approach can classify about 85% of software modules correctly. The result also indicates that fault-prone modules can be detected relatively low cost at an early stage.