Research Of Public Meteorological Service Platform Test Case Generation Based On Rough Set

The establishment of the Public Meteorological Service Platform provides the relevant meteorological objective data for the country, provinces and cities. In order to ensure the platform can be better for meteorological service, improve software reliability, the quality of software must be strictly. The test cases were designed according to the input variables, which resulted in coverage of the program reaches a certain level, the number of test cases become quite large, thereby increasing the complexity of the test. In order to save time and resources, and improve test efficiency, test case must be reduced.Rough set is a kind of set theory, which studies imprecise and uncertainty knowledge. It is mainly used for knowledge reduction and dependent analysis; get the inherent law from a given approximate domain directly. Considering the fitness between the attribute reduction method of rough set and selection of key input variables, put forward introduce reduction method of rough set into software testing. In order to ensure the premise of best test, explore a highly efficient test generation method.This paper used rough set reduction theory and introduced it into the meteorological software testing, extracted key input variables from the input variables of public meteorological service platform, and generated test cases with the main parameters. On the basis of researching and analyzing the existing data reduction algorithm, proposed a new algorithm for attribute reduction and attribute value reduction. Put forward a test case generation method of Public Meteorological Service Platform, it designed a test case reduction system based on rough set, delete the redundant data in the test case. That reduce the input of the test case, optimize the design methods of test case, and improve the efficiency of the test.