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

arXiv:2604.07789 · cs.MA, cs.CL, cs.SE · Submitted 2026-04-09 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents".

Jane: The gist The work introduces 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.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Okay, so we've seen the title and authors of "ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents." It’s about measuring how much those five critical information signals actually contribute to agent performance.

Jane: The paper is tackling this by introducing Oracle-SWE, which is this unified method for isolating and extracting those oracle information signals from SWE benchmarks to quantify the impact of each signal on agent performance >

Lu: What's interesting is that they aren't just looking at these signals in isolation; they are trying to measure their potential contribution upper bounds to overall task success, which helps guide research priorities >

Meng: So it’s essentially creating a hierarchy for what matters in terms of improving an agent's ability to fix code, based on how much information we feed it >

Lalam: They suggest a specific order for SWE-bench, saying that the contribution order is Reproduction Test greater than Execution Context, which is tied with Edit Location, then API Usage and Regression Test >

The paper's summary: Tom: The summary of this paper shows they are looking at how current language model agents often struggle with ambiguous issue descriptions and benefit a lot from explicit failure signals like those high-quality tests produce >

Jane: They are uncertain whether the signals agents find when their capabilities improve can actually become the ground truths and evaluation criteria for those oracle signals >

Lu: So the paper proposes a validation experiment. In Stage one you prompt an agent to extract each factor using its own definition, and in Stage two that same agent tries to solve the task using those extracted factors > <ref:2604.07789#pg1>

Meng: That sounds like a solid way to check if what agents learn when they get better actually aligns with what we consider the ideal information >

Lalam: The paper also notes that native error stack traces are very informative but often unavailable, and custom stacks are used when those native traces aren't present >

The paper's improvements: Tom: Now they discuss how this work improves on previous research by formally defining these five factors through a literature review and proposing a generalized extraction method for multiple SWE benchmarks >

Jane: They are also quantifying the upper bounds of the contribution of these five signals to overall task success, which is meant to guide where researchers should focus their efforts >

Lu: The paper suggests that combining certain factors yields substantial gains, specifically that combining Reproduction Test, Edit Location, and API Usage gives substantially larger gains than any single factor on its own >

Meng: That's a concrete finding for deployment. It tells us we shouldn't optimize one part of the agent pipeline in isolation; we need to look at those combinations >

Lalam: And they conclude by saying that all four model-bench pairings reach at least ninety-seven percent success when all five factors are combined, which supports the idea that this set of factors is complete for resolving SWE benchmarks >

Conclusion: Tom: So to wrap up, this paper on ORACLE-SWE gives us a framework for prioritizing future research directions by quantifying the potential impact of perfect intermediate information >

Jane: The main implication is that improving multiple factors jointly is more effective than optimizing them in isolation when working with these SWE agents >

Lu: They stress that high-quality reproduction tests that capture corner cases are the most influential signal, which helps guide our approach to guiding agent repair processes >

Meng: And they point out that execution context like code graph is only useful when we have those error stack traces available, so we need to focus on getting better data in those specific scenarios >

Lalam: The final success rate of ninety-seven percent with all five factors combined really supports the completeness of this factor set for resolving SWE benchmarks >

Microsoft · Georgia Institute of Technology

cs.MA, cs.CL, cs.SE

Submitted: 2026-04-09

Updated: 2026-10-15

Comments: Under peer review; 31 pages, 9 figures, 11 tables

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 92/100

The gist: The gist The work introduces 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.

Key concepts

Edit Location: This signal precisely identifies where code modifications happen in a correct fix. It includes details like the file path, the specific class or function name involved, and the exact line numbers that were changed. This helps agents understand the scope of their edits.
Execution Context: This factor captures surrounding code that is relevant when reproducing an issue. It is derived from error stack traces and shows which code sections call or are called by the critical locations where a bug occurs, providing necessary context for understanding failures.
Reproduction Test: This involves defining the exact command needed to run tests and listing which specific tests must pass. Crucially, it includes the full source code of these reproduction tests, giving agents concrete steps to replicate and verify a bug scenario.
API Usage: This tracks all function calls that are either modified or newly introduced in the ground truth patch. It details the function names, their arguments, and where they should be placed in the code. This helps agents learn how to correctly use or change software interfaces.

Terminology

Benchmark — 40× in this paper · explained in 2 episodes
Trace — 33× in this paper · explained in 2 episodes
Language Model — 8× in this paper
Ablation — 7× in this paper · explained in 1 episode
Core — 5× in this paper · explained in 2 episodes
Ablation Study — 3× in this paper · explained in 3 episodes
Agentic Workflows — 3× in this paper
Editor — 3× in this paper · explained in 2 episodes
Fine-Tuning — 3× in this paper · explained in 5 episodes
Reinforcement Learning — 3× in this paper · explained in 4 episodes
Verification — 3× in this paper · explained in 2 episodes
Agent Trajectories — 2× in this paper
Agentic System — 2× in this paper · explained in 2 episodes
Failure Modes — 2× in this paper · explained in 3 episodes
AI Agents — 1× in this paper · explained in 2 episodes
Accuracy — 1× in this paper · explained in 1 episode
Causal Reasoning — 1× in this paper · explained in 2 episodes
Evaluation Framework — 1× in this paper · explained in 2 episodes
Generalization — 1× in this paper · explained in 13 episodes
Graph Construction — 1× in this paper · explained in 3 episodes
Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes

Summary

The gist The work introduces 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.

Identifying Critical Information Factors

This research formally defines five critical information factors previous works have focused on through comprehensive literature review and proposes a method to extract ground truths of the 5 factors <ref:2604.07789#pg14>. These factors are categorized into five major categories: Reproduction Test, Regression Test, Edit Location, Execution Context, and API Usage <ref:2604.07789#pg2>. The extraction method is generalized to multiple SWE benchmarks to benefit dataset analysis <ref:2604.07789#pg14>.

Quantifying Upper Bounds through Oracle Analysis

The study estimates the potential contribution upper bounds of the five signals to overall task success, which guides research priorities <ref:2604.07789#pg14>. The contribution order for SWE-bench is Reproduction Test > Execution Context ∼ Edit Location > API Usage > Regression Test, while for SWE-bench-Live and Pro, the order becomes Reproduction Test > Execution Context ∼ API Usage > Edit Location > Regression Test <ref:2604.07789#pg14>.

Agent-Based Validation of Oracle Insights

The work designs a validation experiment to estimate the contribution of the five factors in real-world issue resolution <ref:2604.07789#pg14>. In Stage 1, an agent is prompted to extract each factor using the same definition and format as the oracle signal, while in Stage 2, it attempts to solve the task using those extracted factors <ref:2604.07789#pg14>.

Oracle Information Signals

The five critical information factors are defined as follows:

Edit Location: The code regions where modifications occur in the gold patch, including file path, class/function name, and line range <ref:2604.07789#pg4>.
Execution Context: Code contexts that call or are called by critical locations when reproducing an issue in an SWE task, derived from error stack traces <ref:2604.07789#pg12>.
Reproduction Test: The command required to execute reproduction tests, the list of reproduction test names that must pass, and the full source code of those test functions <ref:2604.07789#pg13>.
Regression Test: The command to execute original regression tests in a repo and the list of tests that must remain passed after the fix (Pass-to-Pass cases) <ref:2604.07789#pg14>.
API Usage: All function calls modified or newly introduced in the ground truth patch, including function name, arguments, approximate region where it should be used, and corresponding function definition <ref:2604.07789#pg14>.

Contribution Patterns and Findings

The experimental results reveal that High-quality reproduction tests that capture corner cases are the most influential signal <ref:2604.07789#pg6>. The ordering of factors is highly consistent across benchmarks, suggesting oracle contributions can indicate the contribution order of agent-extracted signals as agentic capabilities improve <ref:2604.07789#pg6>. Furthermore, Edit-location and API usage becomes significantly more effective when paired with each other or with Reproduction <ref:2604.07789#pg6>. The analysis shows that native error stack traces are highly informative but often unavailable, while custom stacks are used when native error stacks are not available <ref:2604.07789#pg12>.

Validation and Cost Analysis

The validation study confirms that the ordering aligns with oracle patterns, suggesting oracle contributions can indicate the contribution order of agent-extracted signals as agentic capabilities improve <ref:2604.07789#pg6>. The cost analysis indicates that Reproduction Test is cheapest to extract on SWE-benchVerified, while Edit Location is cheapest on SWE-bench-Live and Pro <ref:2604.07789#pg14>. Overall, the results suggest that combining Reproduction Test, Edit Location, and API Usage yields substantially larger gains than any single factor alone <ref:2604.07789#pg14>. The study concludes that all four model-bench pairings reach at least 97% success when all five factors are combined <ref:2604.07789#pg6>.

The research provides a framework for prioritizing future research directions in autonomous coding systems by quantifying the potential impact of perfect intermediate information <ref:2604.07789#pg14>. The study also highlights the importance of high-quality reproduction tests in guiding agent repair processes <ref:2604.07789#pg6>. The findings suggest that improving multiple factors jointly is more effective than optimizing them in isolation <ref:2604.07789#pg14>. These insights are crucial for advancing the capability of language model agents on software engineering tasks <ref:2604.07789#pg14>. The paper addresses a key gap of previous studies on SWE agents: the ideal contribution of the five mostly studied signals when each is made perfect, which is critical in guiding directions for agent capability improvement <ref:2604.07789#pg14>. The work delivers three main findings <ref:2604.07789#pg14>. The final success rate with all five factors reaching at least 97% supports the completeness of the factor set in resolving SWE benchmarks <ref:2604.07789#pg6>. These results are generalizable across different models and datasets, supporting the utility of this factor-isolation method <ref:2604.07789#pg6>. The study provides a roadmap for guiding research priorities for autonomous coding systems <ref:2604.07789#pg14>. The paper concludes that High-quality reproduction tests that capture corner cases are the most influential signal <ref:2604.07789#pg6>. High-quality reproduction tests provide precise descriptions of failure conditions, whereas natural language issue descriptions in SWE benchmarks are often ambiguous <ref:2604.07789#pg6>. The paper also notes that Execution context like code graph is more useful only when error stack traces are available <ref:2604.07789#pg6>. The work suggests that oracle contributions can indicate the contribution order of agent-extracted signals as agentic capabilities improve <ref:2604.07789#pg6>. The paper also notes that more than 4 instances unsolved by either the strong or weak model alone are solved with agent-generated Reproduction Tests <ref:2604.07789#pg14>. This work addresses a key gap of previous studies on SWE agents: the ideal contribution of the five mostly studied signals when each is made perfect, which is critical in guiding directions for agent capability improvement <ref:2604.07789#pg14>. The work delivers three main findings <ref:2604.07789#pg14>. The final success rate with all five factors reaching at least 97% supports the completeness of the factor set in resolving SWE benchmarks <ref:2604.07789#pg6>. These results are generalizable across different models and datasets, supporting the utility of this factor-isolation method <ref:2604.07789#pg6>. The study provides a roadmap for guiding research priorities for autonomous coding systems <ref:2604.07789#pg14>. The paper concludes that High-quality reproduction tests that capture corner cases are the most influential signal <ref:2604.07789#pg6>. High-quality reproduction tests provide precise descriptions of failure conditions, whereas natural language issue descriptions in SWE benchmarks are often ambiguous <ref:2604.07789#pg6>. The paper also notes that Execution context like code graph is more useful only when error stack traces are available <ref:2604.07789#pg6>. The work suggests that oracle contributions can indicate the contribution order of agent-extracted signals as agentic capabilities improve <ref:2604.07789#pg6>. The paper also notes that more than 4 instances unsolved by either the strong or weak model alone are solved with agent-generated Reproduction Tests <ref:2604.07789#pg14>. This work addresses a key gap of previous studies on SWE agents: the ideal contribution of the five mostly studied signals when each is made perfect, which is critical in guiding directions for agent capability improvement <ref:2604.07789#pg14>. The work delivers three main findings <ref:2604.07789#pg14>. The final success rate with all five factors reaching at least 97% supports the completeness of the factor set in resolving SWE benchmarks <ref:2604.07789#pg6>. These results are generalizable across different models and datasets, supporting the utility of this factor-isolation method <ref:2604.07789#pg6>. The study provides a roadmap for guiding research priorities for autonomous coding systems <ref:2604.07789#pg14>.

Improvements for AI systems

Bold header: Oracle Information Signal Quantification for Prioritization

This improvement involves introducing Oracle-SWE, a unified method to isolate and extract oracle information signals from SWE benchmarks to quantify the impact of each signal on agent performance. This allows researchers to answer the crucial question: how much could these systems improve if each signal were perfectly accurate?

Bold header: Oracle Ranking for Research Prioritization

The system can be used to guide research directions by establishing a clear contribution order. The paper reveals that "the contribution order is Reproduction Test > Execution Context ∼ Edit Location > API Usage > Regression Test" for SWE-bench, helping prioritize which agentic capabilities need improvement first.

Bold header: Agent-Based Validation of Oracle Insights

A two-stage validation experiment can be implemented where a stronger model for Stage 1 to emulate high-quality signal when agent capabilities improve is used to extract factors, and a weaker model for Stage 2 attempts the task resolution. This validates whether agent extracted signals when agentic capabilities improve have the same contribution order as the oracle ones.

Abstract

Recent advances in Language Model (LM) agents have increasingly focused on repository-level software engineering (SWE) tasks, leading to various agentic workflows, training methods and failure analysis. Interestingly, rather than directly generating or evaluating edit patches, these approaches first examine and improve intermediate information signals collected during agent exploration, including Reproduction Test, Regression Test, Edit Location, Execution Context, and API Usage. Although different studies examine different subsets of these signals, no work has systematically compared their relative effectiveness in improving final task success or measured the remaining space for improvement in each signal. To address this gap, this work quantifies the contributions of both oracle and agent-extracted signals to final task success and measures the gap between current agent-found signals and their oracle counterparts. We hope our findings could suggest future research emphasis in SWE agents. We release our source code and oracle-signal dataset as a reusable methodology to benefit future signal analysis of SWE agents.