Analysis device and method for detecting variable vulnerability in software using machine learning model

A method of detecting a variable vulnerability in software using a machine learning model, the method comprising:

• receiving, by an analysis device, a source code of a program to be analyzed;
• replacing, by the analysis device, call functions, variable names, and call stack functions in an execution log which is generated during execution of the source code, with certain identifiers (IDs) to preprocess the execution log;
• analyzing, by the analysis device, the preprocessed execution log through a pretrained first learning model to classify whether each pair of a global variable and a call function is at an initialization location;
• analyzing, by the analysis device, the preprocessed execution log through a pretrained second learning model to estimate a maximum value and a minimum value of the global variable;
• and
• determining, by the analysis device, whether the global variable is vulnerable on the basis of (i) whether the global variable has been initialized or an initialization location of the global variable determined according to a classification result of the first learning model and (ii) information output by the second learning model.