Hongyu Zhang

Investigating differences between human-written and machine-generated code for better detection methods.

A new approach enhances commit message quality by leveraging best practices.

A new method integrates local search strategies into Bayesian Optimization for complex problems.

A new method enhances code generation by leveraging project-specific context and compiler feedback.

LogSD improves anomaly detection in log data by focusing on infrequent events.

Exploring the concerns around code usage in AI model training.

Counterfactual reasoning enhances understanding of vulnerabilities in code.

A new method for effective watermarking in AI-generated code.

This study looks at using natural language for easier data visualization.

BARO offers advanced solutions for detecting issues in microservice systems.

OptLLM aids users in choosing the best LLMs for budgets and tasks.

Innovative method improves bug fixing efficiency using LLM-generated patches.

LogBatcher simplifies log parsing using large language models without prior training.

Examining challenges and advancements in root cause analysis for microservices.

Large Language Models enhance code summarization assessments with creative evaluations.

New framework improves security patch detection for users and software.

Discover how segmented deep hashing transforms code retrieval for developers.

Advanced methods are changing how we optimize complex recipes.

RCAEval offers tools for better fault diagnosis in microservice systems.