Open source software projects (OSS) receive a large number of bug reports from various contributors and developers alike, where many planned to be fixed by OSS developers. Given the next release cycle information, OSS users can be more effective and flexible in planning and to fix the bugs that are not to be fixed in the next release. It is therefore vital for OSS users to learn which bugs the OSS developers will fix, unfortunately such information may not be readily available, nor there is a prediction framework exists to serve such an important purpose. In this study, we would like to answer the question 'Will this bug be fixed by the next release?', this is addressed by building a bug fixing prediction model based on the characteristics of a bug-related metric and by incorporating the progress of bug fixing measures such as status, period and developer metrics to provide aggregated information for the OSS users. The proposed model calculates the deviance of each variable to analyze the most important metrics, and it has been experimented using a case study with Eclipse platform. Result shows a bug fixing prediction model using both base metrics and state metrics provide significantly better performance in precision (139%) and recall (114\%) than the standard model using only base metrics.