arXiv:2606.11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repositories linearly, that is, visiting one directory or file per step. We postulate that this is a structural mismatch for changes that span several subsystems. We compare linear sequential exploration against non-linear, domain-scoped parallel agentic exploration. Using SWE Bench Pro as initial benchmark, we focus on ansible as an exemplar. We construct an approach for persistent-session evaluation
The rapid advancement and adoption of LLMs in software engineering necessitates optimizing their operational efficiency for complex tasks like multi-file changes.
Improving how LLM agents explore and interact with large codebases will significantly enhance their utility and accelerate software development, impacting productivity across industries.
This research suggests a shift from linear to non-linear, domain-scoped exploration for LLM agents, potentially making them more effective in resolving intricate software issues.
- · Software developers
- · AI agent developers
- · SaaS companies utilizing AI for code generation/maintenance
- · Companies relying on outdated software maintenance practices
- · Linear-exploration LLM agent models
More efficient and accurate LLM-driven software modifications across complex systems.
Accelerated iteration cycles for software products and increased reliance on AI agents for critical development tasks.
The role of human software engineers evolves further towards architecting and overseeing AI-driven development workflows, rather than direct implementation.
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Read at arXiv cs.AI