The current test case reduction methods can not improve the effectiveness of fault localization, and the current fault localization approaches do not fully analyze the dependency of program elements. To solve these problems, this study proposes an automatic fault localization approach combining test case reduction and joint dependency probabilistic model. Different from the usual test case reduction approach, the failed test cases are fully considered in the proposed test cases reduction method based on execution path in order to provide effective test cases for fast and accurate fault localization. This paper defines a novel statistical model—Joint dependency probabilistic model. In this model, the control dependency and data dependency between program elements, the execution states of each statement are analyzed. An automatic fault localization approach is presented based on joint dependency probabilistic model. It ranks the suspicious statements by calculating the joint dependency suspicion level of the statement. Experimental results show that this approach is more effective than current state-of-art fault-localization methods such as SBI, SOBER, Tarantula, and RankCP.