Fault localization is an essential precondition for repairing in software development. To this end, researchers have proposed automated fault localization (AFL) methods to facilitate the task. Such approaches have taken full advantage of information such as the execution tracks and execution results of given test cases and receive significant effectiveness in reducing the difficulty of fault localization. In competitive crowdsourcing software development, one task could receive multiple competitive implementations (solutions). This study proposes a novel approach for AFL in crowdsourcing software engineering. The key insight of the proposed approach is that when locating faulty statements in a program, it regards competitive implementations as reference programs. By searching for reference statements in reference programs for each statement in buggy program, it calculates the suspicious score of the statement by leveraging its references. Given a set of test cases and a buggy program, the test scenario is run and the initial suspicious score for each statement in the buggy program is calculated by wildly used SBFL approach. After that, suspicious score of each statement is adapted according to its similarity with statements in competitive implementations. The proposed approach is evaluated on 118 real word buggy programs that are accompanied with competitive implementations. The evaluation results suggest that compared with SBFL approaches, the cost of fault localization is reduced by more than 25%.