SWE-Explore: Benchmarking How Coding Agents Explore Repositories

2026-06-05 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

SWE-Explore is a new benchmark designed to evaluate the repository exploration capabilities of coding agents, addressing limitations in existing benchmarks like SWE-bench that treat tasks as binary prediction problems. This benchmark isolates fine-grained agent capabilities such as repository understanding and code localization. SWE-Explore covers 848 issues across 10 programming languages and 203 open-source repositories. For each instance, it provides line-level ground truth derived from independent agent trajectories that successfully solved the same issue, identifying the specific code regions consulted. The benchmark evaluates exploration based on coverage, ranking, and context-efficiency, demonstrating these metrics correlate with downstream repair behavior. Findings indicate that agentic explorers significantly outperform classical retrieval methods, with line-level coverage and efficient ranking being crucial differentiators for modern explorers.

Key takeaway

For AI Engineers developing or evaluating coding agents, you should prioritize benchmarks like SWE-Explore that isolate repository exploration capabilities. This shift from holistic task evaluation allows you to precisely identify and improve agent performance in critical areas like line-level code localization and efficient context retrieval. Focus your development efforts on enhancing line-level coverage and ranking efficiency, as these are key differentiators for agentic explorers and directly impact downstream code repair success.

Key insights

SWE-Explore benchmarks coding agents' repository exploration, revealing agentic methods surpass classical retrieval in line-level code localization.

Principles

• Repository exploration is a critical, isolatable agent capability.
• Line-level coverage and efficient ranking differentiate top explorers.
• Exploration metrics track downstream code repair behavior.

Method

SWE-Explore derives line-level ground truth from successful agent trajectories, asking explorers to rank relevant code regions under a fixed line budget.

In practice

• Evaluate agentic explorers for superior code localization.
• Focus on line-level coverage in agent development.
• Prioritize efficient ranking for context retrieval.

Topics

• SWE-Explore
• Coding Agents
• Repository Exploration
• Code Localization
• Benchmarking
• Software Engineering

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.