The application of machine learning in software testing

Similar to human learning , For machine learning , The size and quality of training samples are crucial , Therefore, we must carefully complete a large number of software test data preparation . Machine learning is widely used in software testing , The following three aspects are mainly discussed ：

（1） Software test design recommendation

In terms of test design , Machine learning can analyze and mine previous test data , Discover software requirements 、 The internal relationship between test requirements and test design , By comparing the test results of different test schemes , Especially some failure lessons , To provide testers with suggestions for finding better test design , Avoid repeating similar mistakes .

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（2） Use pattern recognition

User usage patterns are of great significance for the formulation of testing strategies such as graphical user interfaces . Combine image and other user interface processing technologies , Machine learning can collect 、 inductive 、 Analyze the usage patterns of different types of users , It can automatically generate and execute test cases with higher coverage , It can also affect software quality based on user behavior , For example, ease of use 、 Easy to learn, etc .

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（3） Software vulnerability testing

For many application systems , The internal vulnerability of software is a key factor affecting the extensive deployment of the system . Machine learning can statically analyze code , Identify potential paths that may cause the software to enter a dangerous state , Provide basis for programmers to improve design .