2 citations · 3 across the 12 of their papers we have counts for
16 papers
VisualTrust: Video Reasoning under On-Screen Claim Framing
Huachao Zhu, Junjielong Xu, Sicheng Li +5
On-screen text can describe what a video shows, or it can frame what the video is supposed to mean. Current benchmarks treat both alike, testing whether a model can read the text r…
CodeLift: Your Test Suite Is Secretly a Dense SWE Curriculum
Junjielong Xu, Huachao Zhu, Alyssa Wu +2
The pass-all-test binary reward has become a standard recipe for training software engineering agents, but its sparsity eliminates difference of partially correct patches. The test…
Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis
Qisheng Lu, Aoyang Fang, Junjielong Xu +5
Automated microservice root cause analysis (RCA) is usually evaluated by whether the final answer localizes the fault source. This is insufficient for on-call site reliability engi…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
Manyi Wang, Junjielong Xu, Pinjia He
SWE-bench-like benchmarks are widely used for evaluating LLM's issue resolution capability. They typically follow a common construction pipeline: each PR (Pull Request) is paired w…
SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark
Boxi Yu, Yang Cao, Yuzhong Zhang +9
The SWE-Bench Verified leaderboard is approaching saturation, with the top system achieving 78.80%. However, we show that this performance is inflated. Our re-evaluation reveals th…
DeLog: An Efficient Log Compression Framework with Pattern Signature Synthesis
Siyu Yu, Yifan Wu, Junjielong Xu +8
Parser-based log compression, which separates static templates from dynamic variables, is a promising approach to exploit the unique structure of log data. However, its performance…
MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning
Jianbo Yu, Yixuan Li, Hai Xu +5
Log parsing converts semi-structured logs into structured templates, forming a critical foundation for downstream analysis. Traditional syntax and semantic-based parsers often stru…
SWE-Manager: Selecting and Synthesizing Golden Proposals Before Coding
Boyin Tan, Haoning Deng, Junyuan Zhang +3
Large language model (LLM) research in software engineering has largely focused on tasks such as code generation and bug repair. In practice, teams often draft multiple candidate p…
A Goal-Driven Survey on Root Cause Analysis
Aoyang Fang, Haowen Yang, Haoze Dong +3
Root Cause Analysis (RCA) is a crucial aspect of incident management in large-scale cloud services. While the term root cause analysis or RCA has been widely used, different studie…
Scalable Supervision for Software Agents via Patch Reasoning
Junjielong Xu, Boyin Tan, Xiaoyuan Liu +3
While language model agents have advanced software engineering, existing test-based supervision is limiting its scalability on real-world issues. The reason is twofold: (1) high-co…
Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards
Youliang Yuan, Qiuyang Mang, Jingbang Chen +7
In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. This is evidenced by a high…
Rethinking the Evaluation of Microservice RCA with a Fault Propagation-Aware Benchmark
Aoyang Fang, Songhan Zhang, Yifan Yang +7
While cloud-native microservice architectures have revolutionized software development, their inherent operational complexity makes failure Root Cause Analysis (RCA) a critical yet…
AL-Bench: A Benchmark for Automatic Logging
Boyin Tan, Junjielong Xu, Zhouruixing Zhu +1
Logging, the practice of inserting log statements into source code, is critical for improving software reliability. Recently, language model-based techniques have been developed to…
Repo2Run: Automated Building Executable Environment for Code Repository at Scale
Ruida Hu, Chao Peng, Xinchen Wang +2
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-co…
Aligning the Objective of LLM-based Program Repair
Junjielong Xu, Ying Fu, Shin Hwei Tan +1
Large language models (LLMs) have achieved decent results on automated program repair (APR). However, the next token prediction training objective of decoder-only LLMs (e.g., GPT-4…
Hue: A User-Adaptive Parser for Hybrid Logs
Junjielong Xu, Qiuai Fu, Zhouruixing Zhu +4
Log parsing, which extracts log templates from semi-structured logs and produces structured logs, is the first and the most critical step in automated log analysis. While existing…