  Recent advancements in code-fluent Large Language Models (LLMs) enabled the
research on repository-level code editing. In such tasks, the model navigates
and modifies the entire codebase of a project according to request. Hence, such
tasks require efficient context retrieval, i.e., navigating vast codebases to
gather relevant context. Despite the recognized importance of context
retrieval, existing studies tend to approach repository-level coding tasks in
an end-to-end manner, rendering the impact of individual components within
these complicated systems unclear. In this work, we decouple the task of
context retrieval from the other components of the repository-level code
editing pipelines. We lay the groundwork to define the strengths and weaknesses
of this component and the role that reasoning plays in it by conducting
experiments that focus solely on context retrieval. We conclude that while the
reasoning helps to improve the precision of the gathered context, it still
lacks the ability to identify its sufficiency. We also outline the ultimate
role of the specialized tools in the process of context gathering. The code
supplementing this paper is available at
https://github.com/JetBrains-Research/ai-agents-code-editing.
