REVIEW 13 cited by
Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution. Deployed in TONGYI Lingma, an IDE-based coding assistant developed by Alibaba Cloud, LingmaAgent addresses the limitations of existing LLM-based agents that primarily focus on local code information. Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy enabling agents to explore and understand entire repositories. We guide agents to summarize, analyze, and plan using repository-level knowledge, allowing them to dynamically acquire information and generate patches for real-world GitHub issues. In extensive experiments, LingmaAgent demonstrated significant improvements, achieving an 18.5\% relative improvement on the SWE-bench Lite benchmark compared to SWE-agent. In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9\% of in-house issues faced by development engineers, and solved 43.3\% of problems after manual intervention. Additionally, we have open-sourced a Python prototype of LingmaAgent for reference by other industrial developers https://github.com/RepoUnderstander/RepoUnderstander. In fact, LingmaAgent has been used as a developed reference by many subsequently agents.
Forward citations
Cited by 13 Pith papers
-
On the Road to Personalized Code Intelligence: Portraiting and Assisting Developers Based on Their In-IDE Behaviors
VirtualME is a new infrastructure that continuously extracts and interprets in-IDE developer behaviors to build personalized personas, delivering 33.8% better performance on repository-level knowledge Q&A than generic...
-
SWE-QA: Can Language Models Answer Repository-level Code Questions?
SWE-QA creates a new repository-level code QA benchmark with 576 pairs and an agentic LLM framework, showing promise but open challenges for models handling complex codebases.
-
PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents
A multi-agent repair framework that samples multiple edit locations and iteratively reflects on patch attempts reaches 76.0% Pass@1 on SWE-bench-Verified, up to a 7.8% relative gain over SWE-agent.
-
Retrieval-Oriented Code Representations in Agentic Bug Localization
Role-aware file summaries give the best cost-effectiveness for file-level bug localization, beating file paths by up to 40% Hit@5 at far smaller footprint than raw source.
-
IntentTester: Intent-Driven Multi-agent Framework for Cross-Library Test Migration
IntentTester migrates tests across libraries using TDL abstraction and multi-agent LLM synthesis, achieving 85% correctness and 74% effectiveness versus 51% and 43% for baselines on nine projects in JSON, HTML, and Ti...
-
What Context Does a Coding Agent Actually Need to Act?
At oracle localization on SWE-bench Verified, the edited source itself carries the act signal; structured surrounding context resolves no more issues than dropping it, and compressed context matches whole files at one...
-
SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution
A competitive multi-agent debate over graph-derived fault propagation chains lifts automated issue resolution on SWE-bench Verified to 41.4% pass@1.
-
Git Context Controller: Manage the Context of LLM-based Agents like Git
GCC, a Git-inspired context management layer, is reported to lift a Claude-based agent to 48.00% on SWE-Bench-Lite and to enable a self-replicating CLI that resolves 40.7% of tasks versus 11.7% without it.
-
EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair
ExpeRepair improves LLM-based repository-level program repair by maintaining episodic memory of concrete fixes and semantic memory of abstract insights, reaching 60.3% and 74.6% pass@1 on SWE-Bench Lite and Verified.
-
Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarios
Empirical study of 3977 agent trajectories finds Python execution errors correlate with lower success rates on GitHub issues, flags challenging errors, and reports three confirmed bugs in the SWE-Bench platform.
-
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.
-
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.
-
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 to comment.