Leveraging Program Analysis and машинное обучение for Automated Test Case Generation

This research explores techniques for automatically generating test cases using program analysis and machine learning. The goal is to improve software quality by creating tests that effectively identify potential bugs and vulnerabilities before runtime. By combining static and dynamic analysis with machine learning algorithms, the approach aims to generate meaningful and comprehensive test suites that can enhance code reliability and reduce development costs.