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Yintong Huo

Assistant Professor

Singapore Management University

Singapore

Research Interests

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About

Yintong Huo is an Assistant Professor at Singapore Management University in Singapore. His research focuses on automated logging, user interface code generation, and the application of large language models in software engineering. Recent publications include works on automated detection and repair of logging statements, as well as contextualized generation of logging statements and log parsing.

Articles (21)

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2026

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2025

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2025

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2024

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2024

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2024

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2023

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2023

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2022

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2021

Collaborators (3)

Michael Lyu

Professor

The Chinese University of Hong Kong (CUHK)

HONG KONG

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Wenwei Gu

Assistant Professor

Nankai University

CHINA

View details

David Lo

Singapore Management University

SINGAPORE

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Yintong Huo

Research Interests

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About

Articles (21)

CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency

Larger Is Not Always Better: Exploring Small Open-Source Language Models in Logging Statement Generation

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

From Runnable Code to Shippable Applications: Test-Driven Development for Full-Stack Web Application Generation

UIBenchKit: A Unified Toolkit for Design-to-Code Model Evaluation

Scalable and Adaptive Log-based Anomaly Detection: A Synergistic Approach

Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Fine-grained Feedback

EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation

One Size Does Not Fit All: Revisiting Code Context Engineering for Repository-Level Code Generation

Small Is Beautiful: A Practical and Efficient Log Parsing Framework

ConfLogger: Enhance Systems' Configuration Diagnosability through Configuration Logging

LogFold: Compressing Logs with Structured Tokens and Hybrid Encoding

Divide-and-Conquer: Generating UI Code from Screenshots

LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements

Exploring the Effectiveness of LLMs in Automated Logging Statement Generation: An Empirical Study

Go Static: Contextualized Logging Statement Generation

LILAC: Log Parsing using LLMs with Adaptive Parsing Cache

Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors

AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

LogVM: Variable Semantics Miner for Log Messages

Learning Contextual Causality between Daily Events from Time-consecutive Images

Collaborators (3)

Michael Lyu

Wenwei Gu

David Lo

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