CodeR: Issue Resolving with Multi-Agent and Task Graphs
read the original abstract
GitHub issue resolving recently has attracted significant attention from academia and industry. SWE-bench is proposed to measure the performance in resolving issues. In this paper, we propose CodeR, which adopts a multi-agent framework and pre-defined task graphs to Repair & Resolve reported bugs and add new features within code Repository. On SWE-bench lite, CodeR is able to solve 28.33% of issues, when submitting only once for each issue. We examine the performance impact of each design of CodeR and offer insights to advance this research direction.
This paper has not been read by Pith yet.
Forward citations
Cited by 20 Pith papers
-
Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios
A new benchmark for 0-to-1 CLI tool generation shows state-of-the-art LLMs achieve under 43% success rate with black-box equivalence testing against real oracles.
-
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new benchmark for self-evolving memory in LLM agents across task streams, with baseline ExpRAG and proposed ReMem method that integrates reasoning, actions, and memory updates for continual improvement.
-
OrchestrXR: A Multi-Agent System for Idea-to-Prototype XR Study Authoring
OrchestrXR uses multi-agent orchestration with structured schemas to generate Unity XR study prototypes from ideas, supported by a user study with 12 researchers indicating effective support and intent preservation.
-
On the Reliability of Networks of AI Agents: Density Evolution, Stopping Sets, and Architecture Optimization
Extends density evolution to role-typed factor graphs with nonlinear Boolean verifiers to predict asymptotic unresolved subclaims in AI agent networks under three erasure failure modes.
-
Agentic Coding Needs Proactivity, Not Just Autonomy
Coding agents require a three-level proactivity taxonomy (Reactive, Scheduled, Situation Aware) evaluated by insight policy quality using Insight Decision Quality, Context Grounding Score, and Learning Lift.
-
SAFEdit: Does Multi-Agent Decomposition Resolve the Reliability Challenges of Instructed Code Editing?
SAFEdit reaches 68.6% task success on EditBench code edits by using planner, editor, and verifier agents plus a failure abstraction layer, beating single-model and ReAct baselines.
-
iCoRe: An Iterative Correlation-Aware Retriever for Bug Reproduction Test Generation
iCoRe improves bug reproduction test generation by combining differentiated code/test retrieval, function-call-structure similarity, and iterative generation-to-retrieval feedback, achieving state-of-the-art results o...
-
iCoRe: An Iterative Correlation-Aware Retriever for Bug Reproduction Test Generation
iCoRe improves Fail-to-Pass rates to 42.0% and 52.8% on two bug reproduction benchmarks by using correlation-aware iterative retrieval instead of standard semantic or BM25 methods.
-
REAgent: Requirement-Driven LLM Agents for Software Issue Resolution
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
-
Beyond Fixed Tests: Repository-Level Issue Resolution as Coevolution of Code and Behavioral Constraints
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
-
A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks
NN-RAG extracts 1,289 candidate neural modules from 19 PyTorch repositories, validates 941 of them, and supplies roughly 72% of the novel structures in the LEMUR dataset while enabling cross-repository migration.
-
Process-Centric Analysis of Agentic Software Systems
Graphectory turns stochastic agent trajectories into analyzable graphs, showing that stronger models and successful fixes follow coherent localization-validation steps while failures are chaotic, and online detection ...
-
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and ...
-
Agentless: Demystifying LLM-based Software Engineering Agents
Agentless, a basic three-phase LLM pipeline for bug localization, repair, and validation, outperforms complex open-source agents on SWE-bench Lite with 32% success rate at $0.70 cost.
-
Unlocking Model Potentials Through Adaptive Multi-Agent Scaffolding for Efficient Issue Resolution
icat-agent improves resolution rates on SWE-bench Verified and Pro by 3.6-18.5% over baselines via event-based multi-agent scaffolding and rubric-driven workflow pivoting while using the same models.
-
Exploration Structure in LLM Agents for Multi-File Change Localization
Non-linear domain-scoped parallel LLM agents achieve higher micro F1 than linear exploration and some baselines for multi-file change localization on SWE-bench Pro ansible tasks.
-
What makes a harness a harness: necessary and sufficient conditions for an agent harness
Proposes and tests a constitutive definition of 'agent harness' via conceptual analysis of literature and six real systems.
-
LLM-Based Automated Diagnosis Of Integration Test Failures At Google
Auto-Diagnose applies LLMs to summarize and diagnose root causes of integration test failures, reporting 90.14% accuracy on 71 manual cases and positive adoption after Google-wide rollout.
-
From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
A semi-structured thematic synthesis identifies core challenges in FM selection, alignment, prompting, orchestration, testing, deployment, and cross-cutting concerns like observability for production-ready FMware.
-
Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.