Despite the amount of effort software engineers have been putting into developing fault prediction models. software fault prediction still poses great challenges. This research using machine learning and statistical techniques has been ongoing for 15 years, and yet we still have not had a breakthrough. Unfortunately, none of these prediction models have achieved widespread applicability in the software industry due to a lack of software tools to automate this prediction process. Historical project data, including software faults and a robust software fault prediction tool, can enable quality managers to focus on fault-prone modules. Thus, they can improve the testing process. We developed an Eclipse-based software fault prediction tool for Java programs to simplify the fault prediction process. We also integrated a machine learning algorithm called Naive Bayes into the plug-in because of its proven high-performance for this problem. This article presents a practical view to software fault prediction problem, and it shows how we managed to combine software metrics with software fault data to apply Naive Bayes technique inside an open source platform. (C) 2010 Elsevier Ltd. All rights reserved.