首页 > 市场调查报告书 > 通讯

人工智能

市场调查报告书

商品编码

1616795

AI 代码工具市场机会、成长动力、产业趋势分析与预测 2024-2032

AI Code Tools Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2024-2032

出版日期: 2024年09月10日 | 出版商:

Global Market Insights Inc. | 英文 180 Pages | 商品交期: 2-3个工作天内

价格

简介目录

2023 年，全球人工智慧程式码工具市场估值为 48 亿美元，预计 2024 年至 2032 年复合年增长率为 23.2%。这种增长主要是由于 DevOps 实践的日益采用，特别是持续整合和持续部署（持续集成/持续交付）。 DevOps 专注于改善开发和营运团队之间的协作，而 AI 程式码工具透过自动化测试、部署和监控发挥着至关重要的作用。这些工具符合 DevOps 原则，优化重复性任务，使开发人员能够专注于更复杂的编码，从而实现更快、更可靠的软体交付。随着越来越多的组织采用 DevOps，对支援这些实践的人工智慧增强工具的需求持续增长。

云端运算是推动人工智慧程式码工具市场成长的另一个关键因素。云端平台为部署和管理人工智慧应用程式提供可扩展、灵活且经济高效的解决方案。这对于需要大量运算资源的人工智慧程式码工具尤其重要。透过利用云端基础设施，组织可以有效地开发、训练和部署复杂的人工智慧模型，而不受本地硬体的限制。

云端运算提供的可扩展性使公司能够尝试先进的人工智慧技术，从而增加了对与云端环境无缝整合并优化模型开发和部署的人工智慧工具的需求。根据所提供的产品，市场分为工具和服务。 2023 年，工具细分市场价值约为 31 亿美元。人工智慧驱动的工具变得越来越复杂，可以提供更好的上下文和意图理解，从而产生更准确的编码建议。

市场范围
开始年份	2023年
预测年份	2024-2032
起始值	48亿美元
预测值	301 亿美元
复合年增长率	23.2%

这些工具还可以改善错误检测、增强软体可靠性并减少调试时间。关于部署模型，市场分为本地解决方案和基于云端的解决方案。由于云端服务提供的可扩展性和成本效益，到 2032 年，基于云端的细分市场预计将超过 234 亿美元。云端部署使企业能够处理不同的工作负载、优化资源并最大限度地减少硬体的前期投资，使其成为寻求灵活性和营运效率的公司的首选。全球人工智慧程式码工具市场的35%。该地区是人工智慧进步的中心，拥有大量投资和尖端技术基础设施，推动人工智慧程式码工具在各行业的广泛采用。

产业生态系统分析
供应商格局
- 程式码太开发者
- AI模型开发者
- 云端服务供应商
- 系统整合商
- 终端用户
利润率分析
技术差异化因素
- 模型精度能力
- 集成开发环境 (IDE) 集成
- 模型训练和更新
- 其他的
专利分析
重要新闻和倡议
监管环境
衝击力
- 成长动力
  - 机器学习和深度学习技术的快速进步
  - 各个最终用途产业越来越多地采用人工智慧
  - 对云端运算的需求不断增长
  - DevOps 实践的采用越来越多
- 产业陷阱与挑战
  - 资料隐私和安全问题
  - 程式码准确性和可靠性挑战
成长潜力分析
波特的分析
PESTEL分析

第 4 章：竞争格局

介绍
公司市占率分析
竞争定位矩阵
战略展望矩阵

第 5 章：市场估计与预测：按产品划分，2018 年 - 2032 年

主要趋势
工具
- 程式码产生工具
- 程式码审查和分析工具
- 错误检测工具
- 程式码优化工具
- 其他的
服务
- 专业服务
- 託管工具

第 6 章：市场估计与预测：依技术分类，2018 - 2032

主要趋势
机器学习
深度学习
自然语言处理
生成式人工智慧

第 7 章：市场估计与预测：按部署模型，2018 年 - 2032 年

主要趋势
本地
云

第 8 章：市场估计与预测：按应用划分，2018 年 - 2032 年

主要趋势
数据科学与机器学习
云端服务和开发营运
网页开发
行动应用程式开发
游戏开发
嵌入式系统
其他的

第 9 章：市场估计与预测：按垂直产业，2018 年 - 2032 年

主要趋势
BFSI
资讯科技与电信
卫生保健
製造业
零售与电子商务
政府
媒体与娱乐
其他的

第 10 章：市场估计与预测：按地区划分，2018 年 - 2032 年

主要趋势
北美洲
- 我们
- 加拿大
欧洲
- 英国
- 德国
- 法国
- 义大利
- 西班牙
- 俄罗斯
- 北欧人
- 欧洲其他地区
亚太地区
- 中国
- 印度
- 日本
- 韩国
- 澳新银行
- 东南亚
- 亚太地区其他地区
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 拉丁美洲其他地区
MEA
- 南非
- 沙乌地阿拉伯
- 阿联酋
- MEA 的其余部分

第 11 章：公司简介

Amazon Web Services
CircleCI
Codeium
Datadog
GitHub, Inc.
Google Cloud
IBM
JetBrains sro
Lightning AI
Meta
OpenAI
Replit, Inc.
Salesforce
Snyk
Sourcegraph
Tabnine
Tensorflow

简介目录

Product Code: 7370

The Global AI Code Tools Market was valued at USD 4.8 billion in 2023 and is expected to grow at a CAGR of 23.2% from 2024 to 2032. This growth is largely driven by the increasing adoption of DevOps practices, especially continuous integration and continuous deployment (CI/CD). DevOps focuses on improving collaboration between development and operations teams, and AI code tools play a crucial role by automating testing, deployment, and monitoring. These tools align with DevOps principles, optimizing repetitive tasks and enabling developers to focus on more complex coding, which leads to faster, more reliable software delivery. As more organizations adopt DevOps, the demand for AI-enhanced tools to support these practices continues to rise.

Cloud computing is another key factor driving growth in the AI code tools market. Cloud platforms provide scalable, flexible, and cost-effective solutions for deploying and managing AI applications. This is particularly important for AI code tools that require substantial computational resources. By leveraging cloud infrastructure, organizations can efficiently develop, train, and deploy complex AI models without the constraints of on-premises hardware.

The scalability offered by cloud computing allows companies to experiment with advanced AI techniques, increasing the demand for AI tools that seamlessly integrate with cloud environments and optimize model development and deployment. Based on the offering, the market is segmented into tools and services. In 2023, the tools segment was worth approximately USD 3.1 billion in 2023. The software development industry is experiencing a shift towards automation and AI-powered code generation, which accelerates development cycles and reduces manual coding errors. AI-driven tools are becoming more sophisticated, offering better context and intent understanding, resulting in more accurate coding suggestions.

Market Scope
Start Year	2023
Forecast Year	2024-2032
Start Value	$4.8 Billion
Forecast Value	$30.1 Billion
CAGR	23.2%

These tools also improve bug detection, enhancing software reliability and reducing debugging time. Regarding the deployment model, the market is divided into on-premises and cloud-based solutions. The cloud-based segment is projected to surpass USD 23.4 billion by 2032, thanks to the scalability and cost-efficiency that cloud services offer. Cloud deployment allows businesses to handle varying workloads, optimize resources, and minimize upfront investments in hardware, making it a preferred choice for companies seeking flexibility and operational efficiency.In 2023, North America led the AI code tools market, accounting for around 35% of the global share. This region is a hub for AI advancements, with significant investments and cutting-edge technological infrastructure driving the widespread adoption of AI code tools across industries.

Chapter 1 Methodology & Scope

1.1 Research design
- 1.1.1 Research approach
- 1.1.2 Data collection methods
1.2 Base estimates & calculations
- 1.2.1 Base year calculation
- 1.2.2 Key trends for market estimation
1.3 Forecast model
1.4 Primary research and validation
- 1.4.1 Primary sources
- 1.4.2 Data mining sources
1.5 Market scope & definition

Chapter 2 Executive Summary

2.1 Industry 360° synopsis, 2018 - 2032

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Supplier landscape
- 3.2.1 Code too developers
- 3.2.2 AI model developers
- 3.2.3 Cloud service providers
- 3.2.4 System integrators
- 3.2.5 End-user
3.3 Profit margin analysis
3.4 Technology differentiators
- 3.4.1 Model accuracy capabilities
- 3.4.2 Integrated development environments (IDEs) integration
- 3.4.3 Model training and updates
- 3.4.4 Others
3.5 Patent analysis
3.6 Key news & initiatives
3.7 Regulatory landscape
3.8 Impact forces
- 3.8.1 Growth drivers
  - 3.8.1.1 Rapid advancements in machine learning and deep learning technologies
  - 3.8.1.2 Increasing adoption of AI across various end use industries
  - 3.8.1.3 Increasing demand for cloud computing
  - 3.8.1.4 Growing adoption of DevOps practices
- 3.8.2 Industry pitfalls & challenges
  - 3.8.2.1 Data privacy and security concerns
  - 3.8.2.2 Code accuracy and reliability challenges
3.9 Growth potential analysis
3.10 Porter's analysis
3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Offering, 2018 - 2032 ($Bn)

5.1 Key trends
5.2 Tools
- 5.2.1 Code generation tools
- 5.2.2 Code review & analysis tools
- 5.2.3 Bug detection tools
- 5.2.4 Code optimization tools
- 5.2.5 Others
5.3 Services
- 5.3.1 Professional services
- 5.3.2 Managed tools

Chapter 6 Market Estimates & Forecast, By Technology, 2018 - 2032 ($Bn)

6.1 Key trends
6.2 Machine learning
6.3 Deep learning
6.4 Natural language processing
6.5 Generative AI

Chapter 7 Market Estimates & Forecast, By Deployment Model, 2018 - 2032 ($Bn)

7.1 Key trends
7.2 On-premises
7.3 Cloud

Chapter 8 Market Estimates & Forecast, By Application, 2018 - 2032 ($Bn)

8.1 Key trends
8.2 Data science & machine learning
8.3 Cloud services & DevOps
8.4 Web development
8.5 Mobile app development
8.6 Gaming development
8.7 Embedded systems
8.8 Others

Chapter 9 Market Estimates & Forecast, By Industry Vertical, 2018 - 2032 ($Bn)

9.1 Key trends
9.2 BFSI
9.3 IT & telecom
9.4 Healthcare
9.5 Manufacturing
9.6 Retail & e-commerce
9.7 Government
9.8 Media & entertainment
9.9 Others

Chapter 10 Market Estimates & Forecast, By Region, 2018 - 2032 ($Bn)

10.1 Key trends
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 UK
- 10.3.2 Germany
- 10.3.3 France
- 10.3.4 Italy
- 10.3.5 Spain
- 10.3.6 Russia
- 10.3.7 Nordics
- 10.3.8 Rest of Europe
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 India
- 10.4.3 Japan
- 10.4.4 South Korea
- 10.4.5 ANZ
- 10.4.6 Southeast Asia
- 10.4.7 Rest of Asia Pacific
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Argentina
- 10.5.4 Rest of Latin America
10.6 MEA
- 10.6.1 South Africa
- 10.6.2 Saudi Arabia
- 10.6.3 UAE
- 10.6.4 Rest of MEA