Many quantifiable state-out-of-bound software defects, such as access violations, memory exhaustion, and performance failures, are caused by a large quantity of input data. However, existing dependent data identification and mutation optimization technologies for grey-box fuzzing mainly focus on fixed-length data formats. They are not efficient in increasing the amount of cumulated data required by the accumulated buggy states. This study proposes a differential mutation method to accelerate feature state optimization during the directed fuzzing. By monitoring the seed that updates the maximum or minimum state value of the cumulative defects, the effective mutate offset and content are determined. The frequency is leveraged and the distribution of the effective mutation is offset to distinguish whether the feature value of the defect depends on a fixed field or cumulative data in the input. The effective mutation content is reused as a material in the cumulative input mutation to accelerate the bug reproduction or directed testing. Based on this idea, this study implements the fuzzing tool Jigsaw. The evaluation results on the experimental data set show that the proposed dependency detection method can efficiently detect the input data type that drives the feature value of cumulative defects and the mutation method significantly shorten the reproduction time of the cumulative defect that requires a large amount of special input data.