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相关论文: A Survey on Large Language Models for Code Generat…

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This study investigates the reliability of code generation by Large Language Models (LLMs), focusing on identifying and analyzing defects in the generated code. Despite the advanced capabilities of LLMs in automating code generation,…

软件工程 · 计算机科学 2024-08-27 Ali Mohammadi Esfahani , Nafiseh Kahani , Samuel A. Ajila

Context. Large language models (LLMs) are increasingly applied to code-generating tasks (CGTs) in software engineering. While reported results are promising, the broader effects of such application and their integration into real-world…

软件工程 · 计算机科学 2026-05-26 Muslim Chochlov , Michael English , Jim Buckley

Large Language Models (LLMs) have recently emerged as a focal point of research and application, driven by their unprecedented ability to understand and generate text with human-like quality. Even more recently, LLMs have been extended into…

计算与语言 · 计算机科学 2024-04-03 Kilian Carolan , Laura Fennelly , Alan F. Smeaton

Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in…

软件工程 · 计算机科学 2020-06-16 Triet H. M. Le , Hao Chen , M. Ali Babar

Artificial Intelligence (AI) techniques, especially Large Language Models (LLMs), have started gaining popularity among researchers and software developers for generating source code. However, LLMs have been shown to generate code with…

软件工程 · 计算机科学 2024-11-08 Hyunjae Suh , Mahan Tafreshipour , Jiawei Li , Adithya Bhattiprolu , Iftekhar Ahmed

Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces…

软件工程 · 计算机科学 2023-10-26 Jiexin Wang , Liuwen Cao , Xitong Luo , Zhiping Zhou , Jiayuan Xie , Adam Jatowt , Yi Cai

Large Language Models (LLMs) have shown promising performance in code generation. However, how to reliably evaluate code generated by LLMs remains an unresolved problem. This paper presents CodeJudge, a code evaluation framework that…

机器学习 · 计算机科学 2024-10-04 Weixi Tong , Tianyi Zhang

Recently, the large language models (LLMs) have shown extraordinary ability in understanding natural language and generating programming code. It has been a common practice of software engineers to consult LLMs when encountering coding…

计算与语言 · 计算机科学 2024-01-30 Li Zhong , Zilong Wang

Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire a broad spectrum of open-domain knowledge. Besides, their…

软件工程 · 计算机科学 2025-02-18 Xiaodong Gu , Meng Chen , Yalan Lin , Yuhan Hu , Hongyu Zhang , Chengcheng Wan , Zhao Wei , Yong Xu , Juhong Wang

Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data…

Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM GP, a formalized LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses…

神经与进化计算 · 计算机科学 2024-01-17 Erik Hemberg , Stephen Moskal , Una-May O'Reilly

Large Language Models (LLMs) have become powerful tools for automated code generation. However, these models often overlook critical security practices, which can result in the generation of insecure code that contains…

软件工程 · 计算机科学 2025-07-01 Hao Yan , Swapneel Suhas Vaidya , Xiaokuan Zhang , Ziyu Yao

The rapid advancement of large language models (LLMs) has revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity…

软件工程 · 计算机科学 2024-11-27 Shenao Wang , Yanjie Zhao , Xinyi Hou , Haoyu Wang

Large language models (LLMs) like GitHub Copilot and ChatGPT have emerged as powerful tools for code generation, significantly enhancing productivity and accelerating software development. However, existing benchmarks primarily focus on…

软件工程 · 计算机科学 2024-09-27 Yixi Wu , Pengfei He , Zehao Wang , Shaowei Wang , Yuan Tian , Tse-Hsun Chen

Large Language Models (LLMs) have shown remarkable capabilities in code generation tasks, yet they face significant limitations in handling complex, long-context programming challenges and demonstrating complex compositional reasoning…

人工智能 · 计算机科学 2025-01-14 Amr Almorsi , Mohanned Ahmed , Walid Gomaa

The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language processing and moving towards multi-modal functionality.…

$ $Large Language Models (LLMs) are being increasingly utilized in various applications, with code generations being a notable example. While previous research has shown that LLMs have the capability to generate both secure and insecure…

Generative artificial intelligence attracts significant attention, especially with the introduction of large language models. Its capabilities are being exploited to solve various software engineering tasks. Thanks to their ability to…

软件工程 · 计算机科学 2026-02-06 Lukas Radosky , Ivan Polasek

Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers often rely on LLMs to assess whether system code implementation…

软件工程 · 计算机科学 2025-08-19 Haolin Jin , Huaming Chen

Large Language Models (LLMs), such as Generative Pre-trained Transformers (GPTs) are revolutionizing the generation of human-like text, producing contextually relevant and syntactically correct content. Despite challenges like biases and…

计算与语言 · 计算机科学 2025-08-04 Alper Yaman , Jannik Schwab , Christof Nitsche , Abhirup Sinha , Marco Huber