In the fast-paced world of software development, keeping tests up to date with code changes can be a real headache. Tests often break when the underlying code evolves, leading to frustrating debugging sessions. But imagine if your tests could fix themselves. New research is making that a reality with an AI-powered approach called SYNTER, designed to automatically repair test cases that break due to code changes. The core problem is that code evolves, but tests don't always keep up. This leads to compilation errors, false positives, and wasted developer time. SYNTER tackles this problem by using Language Server Protocol (LSP) and a neural network to understand the context of code changes. It essentially learns the relationship between lines of test code and specific elements within the larger codebase. The LSP acts as a bridge between the AI and the software project, collecting information about code changes and identifying portions of the test suite that might be affected. The neural network then analyzes these changes, comparing old versions of the code with new ones to understand how tests need to evolve. This allows SYNTER to generate highly targeted fixes without needing extensive retraining. The approach not only fixes the most common compilation errors, but also addresses harder-to-fix problems arising from unexpected side effects of changes to the codebase. Initial trials of SYNTER show promising results, outperforming existing tools and dramatically reducing the time required for fixing broken tests. The tool is expected to improve test stability and maintenance over time. While the current focus is on Java code, the principles behind SYNTER could extend to other languages as well, potentially revolutionizing how developers maintain tests during project evolution.