Approaches to detect fault-prone modules have been studied for a long time. As one of these approaches, we proposed a technique using a text filtering technique. We assume that bugs relate to words and context that are contained in a software module. Our technique treats a module as text information. Based on the dictionary which was learned by classifying modules which induce bugs, the bug inducing prob- ability over a target module is calculated, and it judges whether the given module is a fault-prone module. The predictive granularity of this technique is a module. In this study, we aimed at prediction with the finer gran- ularity of the portion which induces a bug. Specifically, we tried to predict bug inducing changes by using source code differences of bug inducing changes and previous changes and a text filtering technique. Similarly, we tried to bug fixing predict by using source code differences of bug fixing changes and previous changes and a text filtering technique. To show the effectiveness of our approach, we conducted two experiments and compared our approach with fault-prone filtering by applying it to two open source projects, and obtained higher accuracy.