The fault-prone module detection in source code is of importance for assurance of software quality. Most of previous conventional 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. In order to mitigate such difficulties, we propose a novel approach for detecting fault-prone modules using a spam filtering technique. 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. In order to show the usefulness of our approach, we conducted an experiment using source code repository of a Java based open source development. The result of experiment shows that our approach can classify more than 70% of software modules correctly.