Research on the evolutionary trends of software forms empowered by artificial intelligence

doi:10.12267/j.issn.2096-5931.2025.08.009

Abstract

Abstract:

With the rapid advancement of artificial intelligence technology, particularly groundbreaking progress made by large models in domains such as natural language processing and computer vision, the development paradigms, architectural designs, interaction mechanisms, and deployment methods of traditional software are undergoing an unprecedented transformation. This paper aims to explore in depth the impact of artificial intelligence technologies—epitomized by large models—on the evolution of software forms. It systematically analyzes the intrinsic mechanisms through which large models drive software toward intelligent evolution, as well as the core characteristics exhibited by new software forms. Focusing on the requirements for intelligence grading, this paper proposes a software intelligence maturity model and corresponding implementation strategies. Additionally, it elaborates on the technical bottlenecks, security risks, ethical dilemmas, and engineering challenges confronting software evolution in the era of large models, and prospect its future development directions, thereby providing references for theoretical research and practical exploration in the intelligent evolution of software.

Key words: artificial intelligence, large models, software, intelligence, software engineering, software form

CLC Number:

QIN Sisi, YAN Dongwei, QI Kexin, CHENG Yang. Research on the evolutionary trends of software forms empowered by artificial intelligence[J]. Information and Communications Technology and Policy, 2025, 51(8): 64-70.

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URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2025.08.009

http://ictp.caict.ac.cn/EN/Y2025/V51/I8/64

Figures/Tables 4

References 12

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成熟度等级	技术特征	人类参与度
L1:基础智能化	传统机器学习模型、按钮/表单交互	人工主导
L2:辅助智能化	单模态生成、自然语言交互、检索内部知识库	人工较多参与
L3:部分智能化	至少两种模态生成与交互、大模型与工具调用协同、外挂知识库或联网、处理单一领域复杂任务	人工审核输出结果
L4:高度智能化	多模态生成与交互、跨知识库协同、处理跨领域复杂任务	人工设定初始目标
L5:超级智能化	全模态生成与交互、全域知识库调用、自主完成未知领域任务、自主进化、自主迭代	零人工干预

成熟度等级	技术特征	人类参与度
L1:基础智能化	传统机器学习模型、按钮/表单交互	人工主导
L2:辅助智能化	单模态生成、自然语言交互、检索内部知识库	人工较多参与
L3:部分智能化	至少两种模态生成与交互、大模型与工具调用协同、外挂知识库或联网、处理单一领域复杂任务	人工审核输出结果
L4:高度智能化	多模态生成与交互、跨知识库协同、处理跨领域复杂任务	人工设定初始目标
L5:超级智能化	全模态生成与交互、全域知识库调用、自主完成未知领域任务、自主进化、自主迭代	零人工干预

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