XAgent: eXecution-guided Agentic AI for Effective Localization and Resolution of GitHub Issues
Source
Hieu Huynh, Patanamon Thongtanunam, Michael Fu, Bach Le, Kla Tantithamthavorn
Author
Hieu Huynh, Patanamon Thongtanunam, Michael Fu, Bach Le, Kla Tantithamthavorn
Date
Terms in this piece · Glossary
SWE-bench — The standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.
Why it matters
Execution-guided localization instead of relying only on static issue text meaningfully improves autonomous bug-fixing accuracy, a concrete method builders of agentic coding tools can borrow to reduce incomplete or misdirected patches.
Key quotes
“Solely relying on this information can bias LLM reasoning toward the narrow scope of the issue description, leading to incomplete patches that fail to address the underlying issue.”
“XAgent successfully resolves 7 additional issues that the top existing baselines fail to address.”
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