Context Management in Expanding Test Automation Codebases with AI Assistance

Target Audience:

— Test automation engineers

— Performance/load testing engineers

— Software developers

Technology Stack:

— Python, Rust

— Git

— HelixDB

— Helixir-memory framework

— Mem0 framework

— Cursor IDE

Problem: today, an increasing number of automated tests are being generated with AI assistance, which significantly accelerates development speed. However, as the codebase grows, a critical problem emerges: AI models cannot effectively "digest" large projects with numerous tests. They "forget" previous work, create duplicate modules and redundant code.

To address this problem, there are several solutions that can help manage large codebases by preventing AI from forgetting what you're working on and helping preserve not only the context but also the reasoning behind it.

I'm going to examine various formats for storing project context: markdown files, tasks/issues, and memory frameworks.