Software crash is a kind of serious software flaw, which can lead to software crashes. Therefore, testing for software crashes is extremely important in the process of software iteration. In recent years, since a large number of test inputs can be automatically generated to trigger software crashes, fuzzing techniques (such as AFL) are widely used in software testing. Nevertheless, most of root causes of crashes that are generated by this technique are same. In this case, software developers have to classify the test inputs one by one, which brings a lot of redundant work. At present, there are many automated methods for testing input classification, mainly including classification algorithms based on program repair and classification algorithms based on software crash information. The former analyzes the program semantics, and re-runs the test input after replacing the repair templates in the program at runtime, and then classifies the inputs. Since this method requires the preparation of repair templates to be completed artificially, the efficiency of its classification is closely related to the quality of the repair templates. At the same time, the repair efficiency of the software has been greatly affected due to the need to repair the crash and classify the crash. Since certain advantages of the latter, this study proposes a lightweight and efficient test inputs classification algorithm, which uses software crash information. Based on the algorithm of software crash point stack information classification, this study introduces dynamic link library information in analyzing CICELY. By distinguishing system dynamic link library from user dynamic link library and combining with location information of user codes, this study gets the set of functions that are focused by programmers to define the crash based on the user function in the classification. In the end, this study also compares CICELY with some existing classification tools based on program repair and software crash information. The experimental test data sets total 19 projects, and 42 test sets. When comparing with other classification tools, Honggfuzz and CERT BFF, whose main classification algorithms are based on software crash information on the same data set, the numbers of classification results of the two are 2112.89% and 135.05% worse than that of CICELY, proving that the experimental effect of CICELY is greatly improved and has higher accuracy compared with similar algorithms. Compared with the classification algorithm "Semantic Crash Bucketing" based on program repair using the test data set provided in their article, CICELY is worse than it by 4.42%. When using the test set consisting of test inputs corresponding to multiple crashes, CICELY got 3% higher repeatability than it. However, Semantic Crash Bucketing can only classify crashes caused by two kinds of crash inputs, null pointer dereference and buffer overflow, while CICELY is not subject to such restrictions.