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Researcher profile

Xing Hu

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Deep code comment generation

    2018

    During software maintenance, code comments help developers comprehend programs and reduce additional time spent on reading and navigating source code. Unfortunately, these comments are often mismatched, missing or outdated in the software projects. Developers have …

  2. Temporal Tensor Local Binary Pattern: A Novel Local Tensor Time Series Descriptor

    2019 · IEEE Transactions on Industrial Informatics

    Time series is very ubiquitous in both the industrial environment and real-life. Capturing the time dependency is very useful for time series analysis. Although the one-dimensional local binary pattern (1D-LBP) can analyze the Univariate time …

  3. C4

    2022

    During software development, developers introduce code clones by reusing existing code to improve programming productivity. Considering the detrimental effects on software maintenance and evolution, many techniques are proposed to detect code clones. Existing approaches are …

  4. CoLeFunDa: Explainable Silent Vulnerability Fix Identification

    2023

    It is common practice for OSS users to leverage and monitor security advisories to discover newly disclosed OSS vulnerabilities and their corresponding patches for vulnerability remediation. It is common for vulnerability fixes to be publicly …

  5. Where Is Self-admitted Code Generated by Large Language Models on GitHub?

    2024 · arXiv (Cornell University)

    The increasing use of Large Language Models (LLMs) in software development has garnered significant attention from researchers evaluating the capabilities and limitations of LLMs for code generation. However, much of the research focuses on controlled …

  6. Safety Alignment of Large Language Models via Contrasting Safe and Harmful Distributions

    2026 · Proceedings of the AAAI Conference on Artificial Intelligence

    With the widespread application of Large Language Models (LLMs), it has become a significant concern to ensure their safety and prevent harmful responses. While current safe-alignment methods based on instruction fine-tuning and Reinforcement Learning from …