With the increasing complexity of software systems,traditional software testing methods face dual challenges of efficiency and accuracy.This article proposes an automated testing framework based on artificial intelligence(AI),aiming to improve the efficiency and coverage of test case generation through machine learning and natural language processing techniques.This framework integrates multiple AI algorithms and can intelligently generate test cases and predict defects based on software's historical data and structural information.Through actual software project testing,the effectiveness of the framework was verified,and the test results showed that the framework can significantly improve testing efficiency while maintaining a high defect detection rate.
维普
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