In earlier research we identified characteristics of files in large software systems that tend to make them particularly likely to contain faults. We then developed a statistical model that uses historical fault information and file characteristics to predict which files of a system are likely to contain the largest numbers of faults. Testers can use that information to prioritize their testing and focus their efforts to make the testing process more efficient and the resulting software more dependable. In this paper we describe a proposed new tool to automate this prediction process, and discuss issues involved in its design and implementation. The goal is to produce an automated tool that mines the project defect tracking system and that can be used by testers without requiring any particular statistical expertise or subjective judgements.