Abstract: Mutation analysis is widely employed to evaluate the effectiveness of various software testing techniques. Existing mutation analysis techniques commonly insert faults into original programs uniformly, while actual faults tend to be clustered, which has been observed in empirical studies. This mismatch may result in the inappropriate simulation of faults, and thus may not deliver the reliable evaluation results. To overcome this limitation, we proposed a distribution-aware mutation analysis technique in our previous work, and it has been validated that the mutation distribution has impact on the effectiveness result of software testing techniques under evaluation. In this paper, we implement a mutation system called MujavaX to support distribution-aware mutation analysis. Such a system is an extension and improvement on Mujava which has been widely employed to mutation testing for Java programs. A case study is conducted to validate the correctness and feasibility of MujavaX, and experimental results show that MujavaX is able to generate a set of mutants for Java programs with respect to the given distribution model specified by testers.