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Junjielong Xu

4 accepted papers

2026

MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning

AAAI 2026technical

Log parsing converts semi-structured logs into structured templates, forming a critical foundation for downstream analysis. Traditional syntax and semantic-based parsers often struggle with semantic variations in evolving logs and data scarcity stemming from their limited domain coverage. Recent lar

Cited by 0SourcePDFScholar
2026

SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark

ICML 2026poster

The SWE-Bench Verified leaderboard is approaching saturation, with the top system achieving 78.80\%. However, we reveal that this performance is inflated: our re-evaluation demonstrates that one in five "solved" patches from the top-30 agents are semantically incorrect, passing only because weak tes…

Cited by 0SourceScholar
2025

OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?

ICLR 2025poster

Large language models (LLMs) are driving substantial advancements in software engineering, with successful applications like Copilot and Cursor transforming real-world development practices. However, current research predominantly focuses on the early stages of development, such as code generation,…

Cited by 2SourcePDFScholar
2025

Repo2Run: Automated Building Executable Environment for Code Repository at Scale

NeurIPS 2025spotlight

Scaling up executable code data is significant for improving language models’ software engineering capability. The intricate nature of the process makes it labor-intensive, time-consuming and expert-knowledge-dependent to build a large number of executable code repositories, limiting the scalability…

Cited by 0SourcecodeScholar