Abstract: A method for constructing an artificial intelligence (AI) integrated model is provided, including: obtaining a training dataset, an initial graph network model, and a plurality of base models; then iteratively training the initial graph network model by using training data in the training dataset and the plurality of base models, to obtain a graph network model; and then constructing the AI integrated model based on the graph network model and the plurality of base models, where an input of the graph network model is a graph structure consisting of outputs of the plurality of base models. Since the graph network model considers neighboring nodes of each node in the graph structure when processing the graph structure, the graph network model fully considers differences and correlations between the base models when fusing the outputs of the plurality of base models.

Abstract: An automated, self-adaptive framework prioritizes software testing in a consistent and effective manner. A metric evaluates past test execution information for assigning regression testing priority. The metric may be calculated with reference to one or more of the following factors taken in combination: requirement, coverage, history, and cost. The requirement factor considers customer-assigned priority of testing the code, complexity of implementing the code, and proneness of the code to faults. The coverage factor considers code coverage, feature coverage, and common usage rate. The history factor considers previous bug found rate, case stable rate, and priority to calculate. The cost factor considers test case execution time, and step length. A value of each factor for one test case is measured according to that test case and is not related to other test cases. The calculation result representing the metric for each test case determines a priority of the test case.

Abstract: An automated, self-adaptive framework prioritizes software testing in a consistent and effective manner. A metric evaluates past test execution information for assigning regression testing priority. The metric may be calculated with reference to one or more of the following factors taken in combination: requirement, coverage, history, and cost. The requirement factor considers customer-assigned priority of testing the code, complexity of implementing the code, and proneness of the code to faults. The coverage factor considers code coverage, feature coverage, and common usage rate. The history factor considers previous bug found rate, case stable rate, and priority to calculate. The cost factor considers test case execution time, and step length. A value of each factor for one test case is measured according to that test case and is not related to other test cases. The calculation result representing the metric for each test case determines a priority of the test case.