Software fault localization tasks begin with the failure of program execution, and locate the root cause of the failure at the code level by analyzing the abnormal internal state during the program execution. The current mainstream spectrum-based and mutation-based fault localization techniques, as well as the most advanced technique SmartFL, utilize coverage information, mutation information, and information represented by program semantics, respectively, as windows to observe the internal state of the program while running. These three types of information are too broad and not targeted enough, and are limited by bottlenecks such as the tie of statement risk values, high mutation cost, and large information scale, respectively. EXPECT, a fault localization technique using exception trigger stream (a new source of information), was recently proposed to monitor the abnormal internal state of the program through the exception handling statements (Try-catch blocks), and achieved promising effectiveness that surpasses the aforementioned mainstream methods. The premise of EXPECT is that the faulty program must contain enough exception-handling statements. However, in the real open-source community, many software programs do not have a good exception handling mechanism, resulting in their codes containing only very sparse or even no exception handling statements, which directly affects the basis on which EXPECT runs. To this end, a software fault localization method based on exception handling statements injection, INSPECT, is proposed in this paper. By automatically injecting temporary exception handling statements into the faulty program as checkpoints for the internal state, designing a more sophisticated algorithm of the calculation for program statements’ risk value, the running scope of EXPECT is expanded to more general programs that do not contain exception handling statements. In other words, INSPECT effectively expands the application scenario of the exception trigger information, which is an efficient source of data for fault localization, and thus delivers higher generalization. Experimental results show that INSPECT obtains better fault localization effectiveness than the state-of-the-art technique with improvements of 95.25%, 55.92%, and 16.65%(simulated faults) as well as 93.39%, 57.54%, and 13.92%(real-world faults) in the best, average, and worst EXAM metrics, respectively, and 311.47%(simulated faults) and 283.31%(real-world faults) in the MRR metric.