Software testing is a commonly used software quality assurance technique. Mutation testing is a fault-based software testing technique that is widely applied to evaluate the sufficiency of test suites and the effectiveness of software testing techniques. However, the cost of mutation testing is extremely high due to the large number of mutants. This study proposes a mutant reduction technique, DFSampling, guided by data flow analysis and designs three heuristic rules. The random selection technique and the path-aware mutant reduction technique (PAMR) are improved in line with these rules. An empirical study is conducted to evaluate the effectiveness of DFSampling and compare DFSampling with the random selection technique and the PAMR technique in terms of effectiveness. The experimental results show that DFSampling is an effective mutant reduction strategy, which can increase the efficiency of mutation testing.