To address the lack of an effective method for generating test cases for Chinese text error correction software， and to measure and optimize the correction performance of software， a multi-user engineering-oriented method was designed， called Selective Generation Method of Test cases for Chinese text error Correction Software （SGMT-CCS）. Two different criteria were defined for evaluating test cases that users can choose from： error quantity density and error type density. SGMT-CCS consists of three modules： test case automatic generation module， test case selection module， and test case priority sorting module. Users can： 1） customize the minimum error interval and the size of the test case set during the automated generation of test cases； 2） customize the minimum error interval and expected value during the selection process； 3） select different criteria for evaluating and prioritizing test cases to meet the requirements of different datasets. Experimental results show that SGMT-CCS can generate effective test cases in a short period of time. The selection module satisfies the user’s customized goals under simulated requirements， and the priority sorting module effectively improves test efficiency in different time periods under different evaluation criteria than before sorting.

Bloom Filter （BF） is a binary vector data structure based on hashing strategy. With the idea of sharing hash collisions， the characteristic of one-way misjudgment and the very small time complexity of constant query， BF is often used to represent membership and as an “accelerator” for membership query operations. As the best mathematical tool to solve the membership query problem in computer engineering， BF has been widely used and developed in network engineering， storage system， database， file system， distributed system and some other fields. In the past few years， in order to adapt to various hardware environments and application scenarios， a large number of variant optimization schemes of BF based on the ideas of changing structure and optimizing algorithm appeared. With the development of big data era， it has become an important direction of membership query to improve the characteristics and operation logic of BF.