activity
20202026
most citedFace It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs

25 citations · 94 across the 53 of their papers we have counts for

collaborators

57 papers

cs.SE2026

Correct Code, Broken Contributions? SWE-CC: Benchmarking Repository Policy Compliance for Coding Agents

Hai Dang Truong, Rayner Goh, Thanh Le-Cong +1

Autonomous coding agents now resolve a substantial share of real-world GitHub issues. However, passing functional tests differs fundamentally from producing a high-quality contribu…

cs.SE2026

Path2Spec: Path-Aware Specification Generation via Large Language Models

Dan Huang, Zhensu Sun, Huihui Huang +4

Formal specifications are critical for program verification, comprehension, and maintenance. However, manually writing them is costly and difficult to scale. Recent studies have sh…

cs.AI2026

LongRCA Bench: Root-Cause Localization in Long-Horizon Agent Trajectories

Yunfei Zhang, Boyu Feng, Changhua Pei +14

In long agent executions, an early error can persist through later actions and checks, while evidence needed to trace its origin is dispersed across the history. Short histories of…

cs.SE2026

Towards Risk-free AI Agent Deployment

Yintong Huo, Rangeet Pan, Abhik Roychoudhury

LLM-based agents are rapidly moving from research prototypes into the core business processes of organizations, but these agents pose deployment risks to security, compliance, and…

cs.SE2026

Does It Render Everywhere? A Study of Cross-Environment Compatibility in MLLM-Generated Webpages

Ziyun Guo, Jingyu Xiao, Yuqiang Sun +1

Multimodal Large Language Models (MLLMs) have been increasingly adopted to automate webpage generation from visual designs (e.g., screenshots). However, existing evaluations are li…

cs.CV2026

CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding

Wenxin Tang, Jingyu Xiao, Zhenyu Liu +6

Rendering source code as images offers a promising way to reduce the input costs of Multimodal Large Language Models (MLLMs). Adjusting image resolution can trade visual token cost…

cs.CR2026

Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

Yikun Li, Ting Zhang, Jiakun Liu +9

Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vul…

cs.SE2026

VisualRepair: Dynamic Tool Calling and Region Focusing for Visual Software Issue Repair

Jingyu Xiao, Zhongyi Zhang, Haoran Hou +4

Automated Program Repair (APR) has witnessed significant progress with the advent of Large Language Models (LLMs). However, as modern software systems increasingly expose rich grap…

cs.SE2026

Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks

Shi Ying Chang, Chiok Yew Ho, Yichen Li +1

AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of these tools are now integrated di…

cs.SE2026

Making Embodied AI Reliable: A Community Agenda from Testing to Formal Verification

Xi Zheng, Dulanga Weerakoon, Yintong Huo +8

Embodied AI systems are increasingly deployed in open-world environments, yet ensuring their reliability remains a fundamental challenge. Drawing on discussions from the AAAI'26 Br…

cs.MA2026

Agent System Operations: Categorization, Challenges, and Future Directions

Zexin Wang, Changhua Pei, Yuanhao Liu +10

As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional sys…

cs.SE2026

CelerLog: Fast Log Parsing via Dynamic Routing

Shiwen Shan, Yintong Huo, Minxing Wang +3

Log parsing is a fundamental step for automated log analysis, which transforms raw log messages into structured formats. Existing syntax-based parsers struggle with complex logs be…

cs.SE2026

From Runnable to Shippable: Multi-Agent Test-Driven Development for Generating Full-Stack Web Applications from Requirements

Yuxuan Wan, Tingshuo Liang, Jiakai Xu +3

Coding agents can generate runnable web applications, but their outputs frequently fail to satisfy functional requirements. Although test-driven development (TDD) offers a principl…

cs.SE2026

UIBenchKit: A unified toolkit for design-to-code model evaluation

Chinh T. Le, Trevor Ong Yee Siang, Jingyu Xiao +2

Recent years have seen substantial progress in automated design-to-code generation, with many methods proposed for generating HTML and CSS from webpage screenshots. However, the ab…

cs.SE2026

TrajAudit: Automated Failure Diagnosis for Agentic Coding Systems

Minxing Wang, Xiaofei Xie, Yintong Huo

Agentic systems have been widely studied to automate coding tasks such as bug fixing and feature implementation. As these systems increasingly operate on complex codebases, underst…

cs.SE2026

AnomalyGen: Enhancing Log-Based Anomaly Detection with Code-Guided Data Augmentation

Xinyu Li, Yintong Huo, Chenxi Mao +4

Log-based anomaly detection is fundamentally constrained by training data sparsity. Our empirical study reveals that public benchmark datasets cover less than 10% of source code lo…

cs.SE2026

Automated Logging Is Language-Sensitive: A Multilingual Benchmark and Empirical Study of LLMs

Renyi Zhong, Yichen Li, Yulun Wu +3

Logging statements are central to debugging, failure diagnosis, and production observability, yet writing them requires developers to decide where to place a logging statement, whi…

cs.SE2026

LogFold: Compressing Logs with Structured Tokens and Hybrid Encoding

Shiwen Shan, Yintong Huo, Hongzhan Zhong +3

Logs are essential for diagnosing failures and conducting retrospective studies, leading many software organizations to retain log messages for a long time. Nevertheless, the volum…

cs.SE2026

ComUICoder: Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Element-wise Feedback

Jingyu Xiao, Jiantong Qin, Shuoqi Li +5

Multimodal Large Language Models (MLLMs) have demonstrated strong performance on the UI-to-code task, which aims to generate UI code from design mock-ups. However, when applied to…

cs.SE2026

Forecasting Developer Environments with GenAI: A Research Perspective

Raula Gaikovina Kula, Christoph Treude, Xing Hu +28

Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ab…

cs.SE2026

Small is Beautiful: A Practical and Efficient Log Parsing Framework

Minxing Wang, Yintong Huo

Log parsing is a fundamental step in log analysis, partitioning raw logs into constant templates and dynamic variables. While recent semantic-based parsers leveraging Large Languag…

cs.AI2026

Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification

Yuxuan Wan, Tianqing Fang, Zaitang Li +5

Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. While the majority of existing efforts focus on enhancing policy…

cs.SE2025

AutoLogger: A Multi-Agent Framework for the End-to-End Automated Logging

Renyi Zhong, Yintong Huo, Wenwei Gu +2

Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlo…

cs.SE2025

Envisioning Future Interactive Web Development: Editing Webpage with Natural Language

Truong Hai Dang, Jingyu Xiao, Yintong Huo

The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate…