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 baselines.
Alibaba lingmaagent: Improving automated issue resolution via comprehensive repository exploration
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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.
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 Time domains.
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.
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, 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.
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
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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.
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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.
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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.