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Large Language Models have quickly become a central component of modern software development workflows, and software practitioners are increasingly integrating LLMs into various stages of the software development lifecycle. Despite the…

软件工程 · 计算机科学 2025-10-13 Italo Santos , Cleyton Magalhaes , Ronnie de Souza Santos

\textit{Background:} The use of large language models in software testing is growing fast as they support numerous tasks, from test case generation to automation, and documentation. However, their adoption often relies on informal…

软件工程 · 计算机科学 2025-10-21 Maria Deolinda Santana , Cleyton Magalhaes , Ronnie de Souza Santos

The rapid advancement of Large Language Models (LLMs) is reshaping software engineering by profoundly influencing coding, documentation, and system maintenance practices. As these tools become deeply embedded in developers' daily workflows,…

软件工程 · 计算机科学 2025-12-29 Vítor Mateus de Brito , Kleinner Farias

Large Language Models (LLMs) represent a leap in artificial intelligence, excelling in tasks using human language(s). Although the main focus of general-purpose LLMs is not code generation, they have shown promising results in the domain.…

软件工程 · 计算机科学 2024-01-30 Sanka Rasnayaka , Guanlin Wang , Ridwan Shariffdeen , Ganesh Neelakanta Iyer

A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content, including grammatically precise sentences, human-like paragraphs, and syntactically accurate code snippets. LLMs can play…

软件工程 · 计算机科学 2023-12-11 Robson Santos , Italo Santos , Cleyton Magalhaes , Ronnie de Souza Santos

The rapid integration of Large Language Models (LLMs) into software engineering practice is reshaping how software testing activities are performed. LLMs are increasingly used to support software testing. Consequently, software testing…

软件工程 · 计算机科学 2026-03-30 Peng Yang , Yunfeng Zhu , Chao Chang , Shengcheng Yu , Zhenyu Chen , Yong Tang

Large language model assistants (LLM-assistants) present new opportunities to transform software development. Developers are increasingly adopting these tools across tasks, including coding, testing, debugging, documentation, and design.…

软件工程 · 计算机科学 2026-03-24 Amr Mohamed , Maram Assi , Mariam Guizani

The use of Large Language Models (LLMs) has drawn growing interest within the scientific community. LLMs can handle large volumes of textual data and support methods for evidence synthesis. Although recent studies highlight the potential of…

Context: Large language models (LLMs) are observed to have a significant positive impact on various software engineering (SE) activities. With improved accessibility, the adoption of powerful LLMs in industry has surged recently. However,…

The use of large language models (LLMs) for qualitative analysis is gaining attention in various fields, including software engineering, where qualitative methods are essential for understanding human and social factors. This study aimed to…

软件工程 · 计算机科学 2025-03-11 Matheus de Morais Leça , Lucas Valença , Reydne Santos , Ronnie de Souza Santos

Large language models (LLMs) are rapidly reshaping software development, but their impact across the software development lifecycle is underexplored. Existing work focuses on isolated activities such as code generation or testing, leaving…

软件工程 · 计算机科学 2025-11-25 Benyamin Tabarsi , Heidi Reichert , Sam Gilson , Ally Limke , Sandeep Kuttal , Tiffany Barnes

Large Language Models (LLMs) have introduced a paradigm shift in interaction with AI technology, enabling knowledge workers to complete tasks by specifying their desired outcome in natural language. LLMs have the potential to increase…

人机交互 · 计算机科学 2025-03-24 Michelle Brachman , Amina El-Ashry , Casey Dugan , Werner Geyer

Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks by leveraging vast amounts of online texts. Unlike conventional models, LLMs can adapt to new domains through prompt…

人工智能 · 计算机科学 2024-06-18 Ming Cheung

Pre-trained large language models (LLMs) have recently emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance…

软件工程 · 计算机科学 2024-03-05 Junjie Wang , Yuchao Huang , Chunyang Chen , Zhe Liu , Song Wang , Qing Wang

Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) have emerged as promising tools to support empirical…

软件工程 · 计算机科学 2026-04-30 Victoria Gomes , Delaney Selb , Fabio Palomba , Rodrigo Spinola , David Lo

Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and…

人机交互 · 计算机科学 2024-04-10 M. Namvarpour , A. Razi

Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption of LLMs in software development, academic research and industry based projects are…

The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across various support tools. We investigate the utility of modern…

人机交互 · 计算机科学 2024-01-31 Tuhin Chakrabarty , Vishakh Padmakumar , Faeze Brahman , Smaranda Muresan

This paper presents a detailed case study examining the application of Large Language Models (LLMs) in the construction of test cases within the context of software engineering. LLMs, characterized by their advanced natural language…

Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the…

软件工程 · 计算机科学 2024-04-11 Xinyi Hou , Yanjie Zhao , Yue Liu , Zhou Yang , Kailong Wang , Li Li , Xiapu Luo , David Lo , John Grundy , Haoyu Wang
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