Imagine an AI assistant that could effortlessly navigate the complex labyrinth of a software project and fix bugs with surgical precision. That's the dream of researchers exploring how to make smaller, more accessible AI models better at understanding and resolving code issues. Large Language Models (LLMs) like GPT-4 have shown promise in tackling complex coding tasks, but their size raises concerns about privacy and cost. Smaller Language Models (SLMs), while more accessible, struggle with the intricate, repository-level understanding needed to fix real-world bugs. A new technique called Repository Structure-Aware Training (ReSAT) is changing the game. By training SLMs on data derived from the structure and history of GitHub repositories, researchers have found a way to significantly boost their code comprehension and bug-fixing abilities. ReSAT essentially teaches the SLMs to 'think' like a developer. It trains them on two key aspects: 1) *Localization*: Pinpointing the exact files, functions, and lines of code relevant to a bug report, much like a developer would during debugging. 2) *Code Editing*: Generating the precise code changes required to fix the issue, based on the localized context. The results are promising. In tests on real-world bug fixes from GitHub, ReSAT-trained SLMs showed a remarkable improvement in their ability to not only understand the codebase but also to generate correct patches. This means faster, more efficient bug fixing without relying on massive, computationally expensive LLMs. While there's still a performance gap between SLMs and the most powerful LLMs, ReSAT represents a significant step toward democratizing AI-powered coding assistance. This approach could lead to more personalized, privacy-preserving coding tools that empower developers of all levels. The research also highlights the potential of using the wealth of open-source data available on platforms like GitHub to train more specialized and efficient AI models for various software development tasks.