ORACLE-SWE: Quantifying the Contribution of Oracle Information Signals on SWE Agents

Authors:Kenan Li, Qirui Jin, Liao Zhu, Xiaosong Huang, Yijia Wu, Yikai Zhang, Xin Zhang, Zijian Jin, Yufan Huang, Elsie Nallipogu, Chaoyun Zhang, Yu Kang, Saravan Rajmohan, Qingwei Lin, Wenke Lee, Dongmei Zhang View PDF HTML (experimental) Abstract:Recent advances in language model (LM) agents have significantly improved automated software engineering (SWE). Prior work has proposed various agentic workflows and training strategies as well as analyzed failure modes of agentic systems on SWE tasks, focusing on several contextual information signals: Reproduction Test, Regression Test, Edit Location, Execution Context, and API Usage. However, the individual contribution of each signal to overall success remains underexplored, particularly their ideal contribution when intermediate information is perfectly obtained. To address this gap, we introduce Oracle-SWE, a unified method to isolate and extract oracle information signals from SWE benchmarks and quantify the impact of each signal on agent performance. To further validate the pattern, we evaluate the performance gain of signals extracted by strong LMs when provided to a base agent, approximating real-world task-resolution settings. These evaluations aim to guide research prioritization for autonomous coding systems. Comments: Under peer review; 37 pages, 10 figures, 5 tables Subjects: Multiagent Systems (cs.MA); Computation and Language (cs.CL); Software Engineering (cs.SE) ACM classes: I.2.7; I.2.5 Cite as: arXiv:2604.07789 [cs.MA] (or arXiv:2604.07789v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2604.07789 arXiv-issued DOI via DataCite (pending registration) Submission history From: Kenan Li [view email] [v1] Thu, 9 Apr 2026 04:37:24 UTC (639 KB)