RepoNav is introduced, a lightweight post-retrieval interface that reorganizes retrieved snippets into a file-centered navigation scaffold that improves function-level localization and narrows the file-to-function gap.
Abstract
Solving repository-level code tasks requires LLM-based agents to use code search tools to navigate large codebases and identify a small set of relevant files and functions. However, current retrieval tools typically return flat lists of isolated code snippets: such lists can surface relevant files, but provide insufficient structure for agents to distinguish the target function from semantically similar alternatives in the same file. We introduce RepoNav, a lightweight post-retrieval interface that reorganizes retrieved snippets into a file-centered navigation scaffold. By presenting compact structural cues and candidate targets, this scaffold guides on-demand file-structure browsing, helping agents compare sibling symbols before selecting a target function. Across diverse models on LocBench, RepoNav improves function-level localization and narrows the file-to-function gap. Controlled ablations demonstrate that these gains come from structured evidence organization rather than simply exposing additional file structure, and the approach also improves performance on a repository-level question-answering benchmark.
SemNav is presented, a framework that leverages deterministic retrieval to seed a broad candidate set and an LLM agent to continually refine that set, thereby combining initial coverage with evidence-guided revision.
FeatLens builds a feature index that links natural-language feature descriptions to function-level code entities and dynamically constructs a task-specific seed graph from the feature index and applies semantic-structural graph reasoning with personalized PageRank to select a compact reasoning graph.
Xu-Tian Li, Bo Xiong, Yi-Feng Zhu et al.· 0 citations
We introduce C2C (From Codebase to Culprit), a framework for precise bug localization that progressively reduces the debugging search space across multiple levels of granularity: files, functions, and lines of code. To mirror developer's natural top-down debugging workflows, C2C integrates semantic retrieval and Hierar...
Ankur Garg, Corey Yang-Smith, Rishav Rishav et al.· 0 citations
CrossCoder is proposed, a cross-repository code generation framework that explicitly incorporates external libraries into the retrieval context through a unified knowledge graph over repository and library entities that consistently improves both functional correctness and robustness to dependency-version changes.
Minh Le-Anh, Nam Le Hai, Quyen Tran et al.· 0 citations
Large language model (LLM)-powered coding agents have made rapid progress in automating software engineering tasks, yet repository-level issue resolution remains challenging. Beyond generating a plausible patch, an agent must localize relevant code across interdependent files and maintain repository context that is bot...
Yun-Xiang Zhang, Hai-Quan Wang, Jia-Wei Guo et al.· 0 citations
A novel chatbot architecture leveraging OpenAI's GPT-4 model for automated extraction and analysis of repository data is introduced, suggesting that this architecture can make repository data more accessible to technical and non-technical audiences through the production of actionable insights.
Muhammad Jawad Chowdhury, Md. Sakib Khan· 1 citation
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