Test Case Selection And Sequencing Method Based On A Combination Of Dynamic And Static Information

Test cases are the core of software testing,which can scientifically manage and organize software testing activities.In the context of continuous integration,the test session puts forward higher requirements for test cases,and researchers have proposed many valuable testing techniques for testing cases in the field.However,most of these methods are mostly distinguished or adjusted for test cases in general.They do not fully analyze the requirements of test case information and test scenarios,and cannot take into account the targeted and effectiveness of the test.Based on in-depth learning and analyzing the optimization method of existing test cases,this article combines the text information of the title of the test case and the historical execution information generated by the regression cycle.This article proposes a new test and sorting method of test cases,which can achieve static information mining and dynamic information search at the same time,and improve the efficiency and quality of software testing.The main research content is as follows:(1)In order to be able to reduce time and resource overhead,to achieve high accuracy and high targeted test case optimization,this article proposes a semantic modelbased static test case selection method-SSM.SSM extracts the theme of each use case through the semantic model,and uses the two-way maximum matching method to perform regular words to test the title of the test case.In order to avoid the division of emerging vocabulary,then use the CRF statistical model to perform secondary words and word marks,and extract the noun sequence.The final calculation case is based on the degree of similarity of Jaccard,and selects test cases that meet the test needs.It not only avoids the results of the human setting parameters in the text theme clustering method,but also greatly improves the effectiveness and accuracy of the test.(2)On this basis,this article proposes a test case sorting method based on historical execution information-dynamic search-DSHEI to further improve test efficiency and defect detection capabilities.DSHEI analyzes the execution information of the previous regression cycle,and proposes a kind of influencing factor HEI.This influence factor can evaluate the defect detection ability and operating expenses of each test case in the next round of return test.After obtaining the impact factor HEI of all use cases,the optimized dynamic search strategy is realized,and the factor is used as the pheromone update rules of the ant colony algorithm,so as to find the test sequence with stronger error detection capacity and lower test cost.In order to verify the effectiveness of SSM and DSHEI,this article experimented with multiple real and rich test case data sets and the actual test data sets of the enterprise.Compared with representative test case selection and test case sorting methods,both SSM and DSHEI can effectively select the use cases and sort them in accordance with the priority.