Directed grey-box fuzzing measures the effectiveness of seeds for detecting the execution path towards the target. In addition to the closeness between the triggered execution and the target code lines, the ability to explore diversified execution paths is also important to avoid local optimum. Current directed grey-box fuzzing methods measure this capability by coverage counting of the whole program. But only a part of the program is responsible for the calculation of the target state. If the new seed brings target irrelevant state changes, it cannot enhance the queue for state exploration. What is worse, it may distract the concentration of the fuzzer and waste time on exploring target irrelevant code logic. To solve this problem, this study provides a valid coverage guided directed grey-box fuzzing method. The static program slicing technique is used to locate the code region that can affect the target state and detect interesting seeds that bring new differences in coverage of this code region. By enlarging the energy of these seeds and reducing others (adjusting power schedule), the fuzzer can be guided to focus on seeds that can help explore different control flow that target depends and mitigate the interference of redundant seeds. The experiment on the benchmark provided shows that this strategy brings significant performance improvement for AFLGO.