SWE-Adept: An LLM-Based Agentic Framework for Deep Codebase Analysis and Structured Issue Resolution
arXiv:2603.01327v2 Announce Type: replace-cross 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 age
The rapid advancement and limitations of large language models (LLMs) in complex software engineering tasks necessitate new architectural frameworks for practical application.
This development addresses a critical barrier for LLMs moving from limited, self-contained coding to real-world, repository-level software development, potentially transforming how software is built and maintained.
The explicit design of specialized agents for localization and resolution within an LLM framework suggests a more structured and effective pathway for autonomous software engineering agents.
- · Software developers
- · AI software companies
- · Large enterprises with complex codebases
- · Junior software engineers
- · Manual code review services
Increased efficiency in codebase analysis and issue resolution for software projects.
Accelerated development cycles and reduced time-to-market for software products.
A significant reduction in the human workforce required for certain software development and maintenance tasks.
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Read at arXiv cs.LG