A machine learning-based intelligent detection method for c program memory leaks

Abstract

The invention discloses an intelligent detection method for C program memory leaks based on machine learning, which is divided into three stages: the model construction stage, constructing two data sets according to existing memory leaks, and extracting memory leak features from the two data respectively , input the memory leak features into the machine learning classifier for training, cross-validate, modify the classifier type and parameters, and select the classifier model with the highest classification accuracy as the classifier model to detect memory leaks. In the program analysis and feature acquisition stage, the source program is pre-analyzed to obtain all memory allocation points o, and then pointer analysis is performed to construct a VFG (ValueFlowGraph) starting from o, and the memory leak characteristics corresponding to each path in the VFG are extracted. In the defect detection and reporting stage, the memory leak features in the program analysis and feature acquisition stages are input into the classifier model in the model construction stage for detection, and it is judged whether there is a memory leak in the path starting from o, and a memory leak report is obtained.

Description

technical field [0001] The invention relates to a memory leak detection method, which uses a program static analysis method and a machine learning algorithm to realize memory leak detection, and belongs to the field of software engineering. Background technique [0002] In computer science, program static analysis refers to the analysis of computer software without actually executing the program. In most cases the analysis is performed on some version of the source code, in other cases it is some form of object code. Common machine learning algorithms include decision trees, random forests, logistic regression, SVM, naive Bayes, etc. The advantage of SVM is that it simplifies the usual classification and regression problems. The complexity of its calculation depends on the number of support vectors, not the dimension of the sample space. Therefore, SVM is not only simple in algorithm but also has good robustness. [0003] At present, the detection of memory leaks mainly ad...

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Problems solved by technology

[0004] The current memory leak detection methods usually use static analysis and dynamic detection. Static analysis has too many false positives, and cannot solve memory leaks caused by some complex structures in C language, and the overhead of dynamic detection is high.

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