Bin Hu is a Lecturer specializing in Software Engineering at Hangzhou Dianzi University. His research primarily focuses on leveraging large language models (LLM) to solve complex challenges in software development, with significant contributions in managing and analyzing code clones.
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Scopus Profile
Dr. Bin Hu is a Lecturer at Hangzhou Dianzi University, Hangzhou, China, specializing in Software Engineering with a focus on large language models (LLMs) and software code analysis. He has authored three research publications and holds an h-index of 1, reflecting initial but impactful contributions to his field. His work has been cited by one other scholarly document, showcasing growing recognition in the research community.
Education
Bin Hu earned his Doctorate (PhD) in Software Engineering from Fudan University, graduating in January 2023. His academic background reflects a strong emphasis on cutting-edge software engineering methodologies.
Research Focus
Bin Hu’s research centers on software engineering applications of large language models. His projects emphasize efficient management of code clones, enabling improved code reuse, quality, and traceability through advanced computational techniques.
Experience
Previously, Bin Hu served as a Research Intern at Tencent’s Software Engineering Group from September 2019 to August 2021. During this period, he contributed to key projects under the National Natural Science Foundation of China (No. 62172099), producing two patents and multiple academic publications.
Research Timeline
Dr. Bin Hu’s research journey spans significant milestones in software engineering and computer science. From 2019 to 2021, he worked as a Research Intern at Tencent, focusing on managing code clones, resulting in two patents and two research papers. After earning his PhD in Software Engineering from Fudan University in 2023, he joined Hangzhou Dianzi University as a Lecturer. In 2024, Dr. Hu published two influential papers: one on feature envy detection through cross-graph semantics in Information and Software Technology and another on meta-reinforcement learning for multi-objective optimization in Complex and Intelligent Systems. His 2025 work in Expert Systems with Applications introduced a cutting-edge framework for code smell detection using AST-based metrics and semantic embeddings. Dr. Hu’s research consistently addresses complex challenges in software engineering, combining theory and practical applications.
Top-Noted Publication
Dr. Bin Hu has authored three notable research articles, showcasing his expertise in software engineering and optimization techniques:
- “Enhancing Structural Knowledge in Code Smell Identification: A Fusion Learning Framework Combining AST-based Metrics with Semantic Embeddings”
Published in Expert Systems with Applications (2025, Vol. 263, Article 125725), this study introduces a novel fusion framework integrating abstract syntax trees (ASTs) and semantic embeddings for detecting code smells.
- “Feature Envy Detection Based on Cross-Graph Local Semantics Matching”
Published in Information and Software Technology (2024, Vol. 174, Article 107515), this paper explores methods for detecting feature envy code smells using cross-graph semantic matching.
- “Dynamic Programming with Meta-Reinforcement Learning: A Novel Approach for Multi-Objective Optimization”
Published in Complex and Intelligent Systems (2024, Vol. 10, Issue 4, pp. 5743–5758), this open-access article presents an innovative meta-reinforcement learning framework for solving multi-objective optimization problems.