SWE-Adept: An LLM-Based Agentic Framework for Deep Codebase Analysis and Structured Issue Resolution

TL;DR

This work proposes SWE-Adept, an LLM-based two-agent framework where a localization agent identifies issue-relevant code locations and a resolution agent implements the corresponding fixes, and introduces agent-directed depth-first search that selectively traverses code dependencies.

Abstract

Large language models (LLMs) exhibit strong performance on self-contained programming tasks. However, they still struggle with repository-level software engineering (SWE), which demands (1) deep codebase navigation with effective context management for accurate localization, and (2) systematic approaches for iterative, test-driven code modification to resolve issues. To address these challenges, we propose SWE-Adept, an LLM-based two-agent framework where a localization agent identifies issue-relevant code locations and a resolution agent implements the corresponding fixes. For issue localization, we introduce agent-directed depth-first search that selectively traverses code dependencies. This minimizes issue-irrelevant content in the agent's context window and improves localization accuracy. For issue resolution, we employ adaptive planning and structured problem solving. We equip the agent with specialized tools for progress tracking and Git-based version control. These tools interface with a shared working memory that stores code-state checkpoints indexed by execution steps, facilitating precise checkpoint retrieval. This design enables reliable agent-driven version-control operations for systematic issue resolution, including branching to explore alternative solutions and reverting failed edits. Experiments on SWE-Bench Lite and SWE-Bench Pro demonstrate that SWE-Adept consistently outperforms prior approaches in both issue localization and resolution, improving the end-to-end resolve rate by up to 4.7%.

Figures (7)

• Figure 1: Overview of Issue Localization framework. (a) Codebase is indexed and represented as code-structure tree in the structured database. Based on this representation, (b) Issue Localization Agent performs search and pinpointing: (b-1) agent-directed depth-first traversal for selective, dependency-aware exploration, with search tools (Table \ref{['localization_tool']}) returning lightweight structural information; (b-2) post-search two-stage filtering (code-preview and location heuristics followed by content-based analysis) for candidate re-ranking and final issue-relevant locations.
• Figure 2: Overview of Issue Resolution framework. (a) Issue Resolution Agent takes identified code locations as input and performs structured issue resolution. The agent is equipped with two CLI (command-line interface)-based tool families (§ \ref{['sec:issue_resolution']}), hypothesis_plan (Table \ref{['resolution_tool_1']}) and hypothesis_git (Table \ref{['resolution_tool_2']}), for planning, progress tracking, and version control. (b) Backend working memory stores structured metadata for hypotheses, to-dos and code-state checkpoints. Both tool families interface with this memory to manage checkpoints for version-control operations, including (a-1) branching to explore alternative solutions (hypotheses) and (a-2) reverting failed edits. (c) The agent merges the selected hypothesis branch after comparing all implemented hypotheses and submits the final patch.
• Figure 3: Venn diagram (left) of resolved-instance overlap between SWE-Adept and SWE-agent; and error breakdowns (right) for instances uniquely failed by each method (e.g., the left bar in the chart represents the 24 instances resolved by SWE-Adept but failed by SWE-agent). Reported results are on SWE-Bench Lite with Claude-Sonnet-4.5.
• Figure 4: Search behavior of Issue Localization Agent and localization accuracy by maximum search depth. (a) Search-tool invocation distribution of Issue Localization Agent. (b) Instance distribution (bars) and function-level localization accuracy (lines) by maximum search depth on SWE-Bench Lite with Claude-Sonnet-4.5.
• Figure 5: Problem-solving behavior of Issue Resolution Agent and resolve rate by number of explored hypotheses. (a) Prevalence of systematic problem-solving behaviors: multi-hypothesis branching, dynamic to-do expansion, and checkpoint-based reversion (behaviors are non-mutually exclusive and may overlap). (b) Instance distribution (bars) and resolve rate (lines) by number of explored hypotheses on SWE-Bench Lite with Claude-Sonnet-4.5.