SWE-Exp: Experience-Driven Software Issue Resolution
Recent advances in large language model (LLM) agents have shown remarkable
progress in software issue resolution, leveraging advanced techniques such as
multi-agent collaboration and Monte Carlo Tree Search (MCTS). However, current
agents act as memoryless explorers - treating each problem separately without
retaining or reusing knowledge from previous repair experiences. This leads to
redundant exploration of failed trajectories and missed chances to adapt
successful issue resolution methods to similar problems. To address this
problem, we introduce SWE-Exp, an experience - enhanced approach that distills
concise and actionable experience from prior agent trajectories, enabling
continuous learning across issues. Our method introduces a multi-faceted
experience bank that captures both successful and failed repair attempts.
Specifically, it extracts reusable issue resolution knowledge at different
levels - from high-level problem comprehension to specific code changes.
Experiments show that SWE-Exp achieves state-of-the-art resolution rate (41.6%
Pass@1) on SWE-bench-Verified under open-source agent frameworks. Our approach
establishes a new paradigm in which automated software engineering agents
systematically accumulate and leverage repair expertise, fundamentally shifting
from trial-and-error exploration to strategic, experience-driven issue
resolution.
688c53f38c434640078cc47c
Silin Chen
688c53f38c434640078cc47d
Shaoxin Lin
688c53f38c434640078cc47e
Xiaodong Gu
688c53f38c434640078cc47f
Yuling Shi
688c53f38c434640078cc480
Heng Lian
688c53f38c434640078cc481
Longfei Yun
688c53f38c434640078cc482
Dong Chen
688c53f38c434640078cc483
Weiguo Sun
688c53f38c434640078cc484
Lin Cao
688c53f38c434640078cc485
Qianxiang Wang
SWE-Exp: Experience-Driven Software Issue Resolution
Recent advances in large language model (LLM) agents have shown remarkable
progress in software issue resolution, leveraging advanced techniques such as
multi-agent collaboration and Monte Carlo Tree Search (MCTS). However, current
agents act as memoryless explorers - treating each problem separately without
retaining or reusing knowledge from previous repair experiences. This leads to
redundant exploration of failed trajectories and missed chances to adapt
successful issue resolution methods to similar problems. To address this
problem, we introduce SWE-Exp, an experience - enhanced approach that distills
concise and actionable experience from prior agent trajectories, enabling
continuous learning across issues. Our method introduces a multi-faceted
experience bank that captures both successful and failed repair attempts.
Specifically, it extracts reusable issue resolution knowledge at different
levels - from high-level problem comprehension to specific code changes.
Experiments show that SWE-Exp achieves state-of-the-art resolution rate (41.6%
Pass@1) on SWE-bench-Verified under open-source agent frameworks. Our approach
establishes a new paradigm in which automated software engineering agents
systematically accumulate and leverage repair expertise, fundamentally shifting
from trial-and-error exploration to strategic, experience-driven issue
resolution.
SWE-Exp enhances software issue resolution by systematically accumulating and leveraging repair expertise from past agent experiences, improving resolution rates.
large language model (LLM) multi-agent collaboration Monte Carlo Tree Search (MCTS) experience bank issue resolution knowledge SWE-bench-Verified open-source agent frameworks