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市场调查报告书
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1438212

到 2030 年 AI 自动化测试市场预测：按组件、部署、组织规模、技术、应用程式、最终用户和地区进行的全球分析

AI Automation Testing Market Forecasts to 2030 - Global Analysis By Component (Testing Type, Service and Electric), Deployment, Organization Size, Technology, Application End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据Stratistics MRC预测，2023年全球人工智慧自动化测试市场规模将达292亿美元，预计2030年将达到953亿美元，预测期内复合年增长率为18.4%。人工智慧自动化测试涉及使用人工智慧和机器学习来增强软体测试过程。实现自动测试用例产生、执行和分析，以提高效率和准确性。人工智慧演算法透过识别模式、预测缺陷和优化测试来减少人工干预。这种方法加快了测试生命週期，确保全面覆盖，并提高了软体发布的品质。该技术简化了测试工作，识别漏洞，有助于提高软体的整体可靠性，并满足现代软体开发实践的需求。

加速软体开发

快速、持续的发布需要高效、及时的测试。自动化测试中的人工智慧可以更快地识别缺陷，提高测试覆盖率，并及早发现错误，从而加快测试生命週期。这种协同效应可确保应用程式得到彻底检验并为加快开发速度做好准备。随着公司优先考虑软体交付和采用的速度和质量，该市场在保持敏捷性、缩短上市时间和提高整体软体可靠性方面发挥关键作用。

实施成本高

组织，尤其是规模较小的组织，可能会因获取人工智慧工具、培训人员和建立必要基础设施所需的高昂前期成本而望而却步。这种财务障碍限制了先进测试技术的使用，并阻碍了更广泛的采用。意识到财务负担可能会导致公司选择传统的测试方法，从而减缓市场扩张。

招募

随着跨装置、平台和配置的软体生态系统变得越来越复杂，人工智慧主导的测试可确保弹性和扩充性。这种适应性解决了不同测试场景带来的挑战，从而提高了效率和全面的测试覆盖范围。寻求敏捷和响应式测试解决方案的公司重视回应动态环境的能力。

缺乏熟练的专业人员

测试和人工智慧方面专家的缺乏阻碍了先进测试技术的成功实施和利用。企业难以充分发挥人工智慧主导测试的潜力，导致实施缓慢且缺乏最佳化。这种稀缺性阻碍了人工智慧自动化测试解决方案的发展，并限制了它们对提高测试效率和整体软体品质的影响。

COVID-19 的影响

虽然向数位转型的转变已经加速，并且对自动化测试解决方案的需求也在增加，但预算限制和资源限制却减缓了采用速度。远距工作情况也凸显了强大的软体测试的重要性以及对人工智慧主导的测试解决方案的兴趣增加。疫情造成高效测试解决方案需求增加和实施挑战的双重影响，对AI自动化测试市场产生微妙影响。

机器学习领域预计将在预测期内成为最大的领域

随着机器学习演算法实现智慧测试脚本生成、动态测试用例优先顺序和自适应测试维护，机器学习领域预计将出现利润丰厚的成长。其结果是更有效的缺陷识别和更大的测试覆盖率。此外，机器学习有助于预测潜在问题、减少误报并自动执行重复测试任务，从而促进市场成长。

预计基于行动的细分市场在预测期内将出现最高的复合年增长率

基于行动的细分市场预计在预测期内将以最高复合年增长率成长，以提高测试效率并确保跨不同行动平台的无缝功能。随着行动应用开发的快速成长，需要严格的测试，而基于行动的人工智慧解决方案提供了更快、更准确的测试过程。随着行动技术的不断发展，整合人工智慧自动化测试对于企业确保行动应用程式健壮可靠并满足最终用户的动态期望至关重要。

比最大的地区

在预测期内，由于自动化测试的显着扩张，预计北美将占据最大的市场占有率。随着行动应用程式变得越来越复杂，人工智慧回归测试的使用越来越多，影响了北美的人工智慧测试。此外，由于技术供应商的存在，美国预计在整个预测期内将取得显着发展。该市场的扩张是由都市化加快、生活方式不断变化、可支配收入增加和技术进步等因素所推动的。

复合年增长率最高的地区：

由于研发费用增加、对自动化测试解决方案的需求不断增长、新产品推出等，预计亚太地区在预测期内将呈现最高的复合年增长率。为了支持市场扩张，中国、日本和印度等亚太国家正在开发和推出新平台和产品。此外，由于对自动化和有效的通讯基础设施测试和维护的需求可能会激增，日本人工智慧驱动的测试技术的使用可能会增加。

提供免费客製化：

订阅此报告的客户可以存取以下免费自订选项之一：

  • 公司简介
    • 其他市场参与者的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 调查来源
    • 主要调查来源
    • 二次调查来源
    • 先决条件

第三章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • 新型冠状病毒感染疾病（COVID-19）的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第五章全球人工智慧自动化测试市场：按组成部分

  • 测试类型
    • 动态测试
      • 功能测试
      • API测试
      • 性能测试
      • 压力测试
      • 回归测试
      • 安全测试
    • 静态测试
  • 服务
    • 专业服务
    • 管理服务
  • 其他组件

第六章 全球人工智慧自动化测试市场：按部署划分

  • 云
  • 本地

第七章 全球人工智慧自动化测试市场：依组织规模划分

  • 大公司
  • 中小企业

第八章全球人工智慧自动化测试市场：依技术分类

  • NLP（自然语言处理）
  • 机器学习
  • MBTA（基于模型的测试自动化）
  • 电脑视觉
  • 其他技术

第九章 全球人工智慧自动化测试市场：按应用分类

  • 基于网路的
  • 移动基地

第十章 全球人工智慧自动化测试市场：依最终使用者分类

  • 资讯科技和通讯
  • 卫生保健
  • BFSI
  • 政府
  • 国防和航太
  • 能源和公共
  • 其他最终用户

第十一章 全球人工智慧自动化测试市场：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲

第十二章 主要进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十三章 公司简介

  • Apexon
  • Applitools
  • Capgemini SE
  • D2L Corp.
  • Functionize Inc.
  • IBM Corporation
  • Keysight technologies
  • Mabl Inc.
  • Micro Focus International Plc
  • Open Text
  • Parasoft
  • Perforce Software, In
  • ReTest GmbH
  • Sauce Labs Inc.
  • Testim
  • testRigor
  • Tricentis
  • UBS Hainer GmbH
Product Code: SMRC25181

According to Stratistics MRC, the Global AI Automation Testing Market is accounted for $29.2 billion in 2023 and is expected to reach $95.3 billion by 2030 growing at a CAGR of 18.4% during the forecast period. AI Automation Testing involves the use of artificial intelligence and machine learning to enhance software testing processes. It enables automated test case generation, execution, and analysis, improving efficiency and accuracy. AI algorithms identify patterns, predict defects, and optimize testing, reducing manual intervention. This approach accelerates the testing lifecycle, ensures comprehensive coverage, and enhances the quality of software releases. The technology streamlines testing efforts, identifies vulnerabilities, and contributes to overall software reliability, meeting the demands of modern software development practices.

Market Dynamics:

Driver:

Accelerated software development

The need for rapid and continuous releases requires efficient and timely testing. AI in automation testing expedites the testing lifecycle, offering quick identification of defects, increased test coverage, and early bug detection. This synergy ensures that applications are thoroughly validated, aligning with the accelerated development pace. As organizations prioritize speed and quality in software delivery, the market experiences heightened adoption, playing a pivotal role in maintaining agility, reducing time-to-market, and enhancing overall software reliability.

Restraint:

High implementation cost

Organizations, particularly smaller ones, may be deterred by the substantial upfront expenses involved in acquiring AI tools, training personnel, and establishing the necessary infrastructure. This financial barrier limits the accessibility of advanced testing technologies, hindering broader adoption. The perceived financial burden could lead businesses to opt for traditional testing methods, slowing down the market expansion.

Opportunity:

Adoption

As software ecosystems become increasingly complex with varied devices, platforms, and configurations, AI-driven testing ensures flexibility and scalability. This adaptability addresses the challenges posed by diverse testing scenarios, leading to improved efficiency and comprehensive test coverage. Organizations seeking agile and responsive testing solutions value the capability to handle dynamic environments.

Threat:

Shortage of skilled professionals

The lack of experts proficient in both testing and AI impedes the successful implementation and utilization of advanced testing technologies. Companies face difficulties in harnessing the full potential of AI-driven testing, leading to delayed or suboptimal adoption. This scarcity hampers the growth of AI Automation Testing solutions, limiting their impact on improving testing efficiency and overall software quality.

Covid-19 Impact

While the demand for automated testing solutions increased due to the accelerated shift towards digital transformation, budget constraints and resource limitations slowed down adoption. Remote working conditions also highlighted the importance of robust software testing, driving interest in AI-driven testing solutions. The pandemic created a dual effect of increased demand for efficient testing solutions and challenges in implementation, resulting in a nuanced impact on the AI Automation Testing market.

The machine learning segment is expected to be the largest during the forecast period

The machine learning segment is estimated to have a lucrative growth, because the machine learning algorithms enable intelligent test script generation, dynamic test case prioritization, and adaptive test maintenance. This results in more effective identification of defects and improved testing coverage. Additionally, machine learning aids in predicting potential issues, reducing false positives, and automating repetitive testing tasks boosting the market growth.

The mobile-based segment is expected to have the highest CAGR during the forecast period

The mobile-based segment is anticipated to witness the highest CAGR growth during the forecast period, as it enhances testing efficiency, ensuring seamless functionality across diverse mobile platforms. The surge in mobile app development demands rigorous testing, and mobile-based AI solutions provide quicker, more accurate testing processes. As mobile technologies continue to evolve, the integration of AI automation testing becomes imperative for businesses to ensure robust and reliable mobile applications, meeting the dynamic expectations of end-users

Region with largest share:

North America is projected to hold the largest market share during the forecast period driven by the notable expansion of automated testing. As mobile apps become more functional, AI regression testing is being utilized more and more, which is impacting AI-enabled testing in North America. Furthermore, because of the existence of technology suppliers, the United States is anticipated to develop greatly throughout the projection period. The expansion of this market is driven by factors such as growing urbanization, evolving lifestyles, increased disposable income, and enhanced technology.

Region with highest CAGR:

Asia Pacific is projected to have the highest CAGR over the forecast period, owing to rising R&D spending, rising demand for automated testing solutions, and the introduction of new products. To support market expansion, Asia Pacific nations like China, Japan, India, and others are developing and introducing new platforms and goods. Additionally a possible upsurge in demand for automated and effective telecom infrastructure testing and maintenance may lead to a rise in the use of AI-enabled testing technologies in Japan.

Key players in the market

Some of the key players in the AI Automation Testing Market include Apexon, Applitools, Capgemini SE, D2L Corp., Functionize Inc., IBM Corporation, Keysight technologies, Mabl Inc., Micro Focus International Plc, Open Text, Parasoft, Perforce Software In, ReTest GmbH, Sauce Labs Inc., Testim, testRigor, Tricentis and UBS Hainer GmbH

Key Developments:

In December 2023, Apexon, a digital-first technology services company, today announced that Microsoft has named it a Solutions Partner for Data and AI. This prestigious accolade follows the company's recent achievements in securing the Microsoft Digital and App Innovation, and Infrastructure Solutions Partner designations

In August 2023, Apexon, has expanded its presence in India by setting up a new facility in Ahmedabad. The new delivery center will leverage the rich engineering talent pool in Ahmedabad and India and further strengthen Apexon's ability to deliver digital and business transformation for its global client base.

In July 2023, Applitools Partners with Sogeti on '2021 State of Artificial Intelligence Applied to Quality Engineering Report. Sogeti will introduce each follow-on section of the full report every two weeks from September to the end of January

Components Covered:

  • Testing Type
  • Service
  • Other Components

Deployments Covered:

  • Cloud
  • On-Premise

Organization Sizes Covered:

  • Large Enterprises
  • Small And Medium-Sized Enterprises

Technologies Covered:

  • NLP (Natural Language Processing)
  • Machine Learning
  • MBTA (Model-Based Test Automation)
  • Computer Vision
  • Other Technologies

Applications Covered:

  • Web-Based
  • Mobile-Based

End Users Covered:

  • IT & Telecommunication
  • Healthcare
  • BFSI
  • Government
  • Defense And Aerospace
  • Energy & Utilities
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI Automation Testing Market, By Component

  • 5.1 Introduction
  • 5.2 Testing Type
    • 5.2.1 Dynamic Testing
      • 5.2.1.1 Functional Testing
      • 5.2.1.2 API Testing
      • 5.2.1.3 Performance Testing
      • 5.2.1.4 Load Testing
      • 5.2.1.5 Regression Testing
      • 5.2.1.6 Security Testing
    • 5.2.2 Static Testing
  • 5.3 Service
    • 5.3.1 Professional Services
    • 5.3.2 Managed Services
  • 5.4 Other Components

6 Global AI Automation Testing Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud
  • 6.3 On-Premise

7 Global AI Automation Testing Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Large Enterprises
  • 7.3 Small And Medium-Sized Enterprises

8 Global AI Automation Testing Market, By Technology

  • 8.1 Introduction
  • 8.2 NLP (Natural Language Processing)
  • 8.3 Machine Learning
  • 8.4 MBTA (Model-Based Test Automation)
  • 8.5 Computer Vision
  • 8.6 Other Technologies

9 Global AI Automation Testing Market, By Application

  • 9.1 Introduction
  • 9.2 Web-Based
  • 9.3 Mobile-Based

10 Global AI Automation Testing Market, By End User

  • 10.1 Introduction
  • 10.2 IT & Telecommunication
  • 10.3 Healthcare
  • 10.4 BFSI
  • 10.5 Government
  • 10.6 Defense And Aerospace
  • 10.7 Energy & Utilities
  • 10.8 Other End Users

11 Global AI Automation Testing Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Apexon
  • 13.2 Applitools
  • 13.3 Capgemini SE
  • 13.4 D2L Corp.
  • 13.5 Functionize Inc.
  • 13.6 IBM Corporation
  • 13.7 Keysight technologies
  • 13.8 Mabl Inc.
  • 13.9 Micro Focus International Plc
  • 13.10 Open Text
  • 13.11 Parasoft
  • 13.12 Perforce Software, In
  • 13.13 ReTest GmbH
  • 13.14 Sauce Labs Inc.
  • 13.15 Testim
  • 13.16 testRigor
  • 13.17 Tricentis
  • 13.18 UBS Hainer GmbH

List of Tables

  • Table 1 Global AI Automation Testing Market Outlook, By Region (2021-2030) ($MN)
  • Table 2 Global AI Automation Testing Market Outlook, By Component (2021-2030) ($MN)
  • Table 3 Global AI Automation Testing Market Outlook, By Testing Type (2021-2030) ($MN)
  • Table 4 Global AI Automation Testing Market Outlook, By Dynamic Testing (2021-2030) ($MN)
  • Table 5 Global AI Automation Testing Market Outlook, By Functional Testing (2021-2030) ($MN)
  • Table 6 Global AI Automation Testing Market Outlook, By API Testing (2021-2030) ($MN)
  • Table 7 Global AI Automation Testing Market Outlook, By Performance Testing (2021-2030) ($MN)
  • Table 8 Global AI Automation Testing Market Outlook, By Load Testing (2021-2030) ($MN)
  • Table 9 Global AI Automation Testing Market Outlook, By Regression Testing (2021-2030) ($MN)
  • Table 10 Global AI Automation Testing Market Outlook, By Security Testing (2021-2030) ($MN)
  • Table 11 Global AI Automation Testing Market Outlook, By Static Testing (2021-2030) ($MN)
  • Table 12 Global AI Automation Testing Market Outlook, By Service (2021-2030) ($MN)
  • Table 13 Global AI Automation Testing Market Outlook, By Professional Services (2021-2030) ($MN)
  • Table 14 Global AI Automation Testing Market Outlook, By Managed Services (2021-2030) ($MN)
  • Table 15 Global AI Automation Testing Market Outlook, By Other Components (2021-2030) ($MN)
  • Table 16 Global AI Automation Testing Market Outlook, By Deployment (2021-2030) ($MN)
  • Table 17 Global AI Automation Testing Market Outlook, By Cloud (2021-2030) ($MN)
  • Table 18 Global AI Automation Testing Market Outlook, By On-Premise (2021-2030) ($MN)
  • Table 19 Global AI Automation Testing Market Outlook, By Organization Size (2021-2030) ($MN)
  • Table 20 Global AI Automation Testing Market Outlook, By Large Enterprises (2021-2030) ($MN)
  • Table 21 Global AI Automation Testing Market Outlook, By Small And Medium-Sized Enterprises (2021-2030) ($MN)
  • Table 22 Global AI Automation Testing Market Outlook, By Technology (2021-2030) ($MN)
  • Table 23 Global AI Automation Testing Market Outlook, By NLP (Natural Language Processing) (2021-2030) ($MN)
  • Table 24 Global AI Automation Testing Market Outlook, By Machine Learning (2021-2030) ($MN)
  • Table 25 Global AI Automation Testing Market Outlook, By MBTA (Model-Based Test Automation) (2021-2030) ($MN)
  • Table 26 Global AI Automation Testing Market Outlook, By Computer Vision (2021-2030) ($MN)
  • Table 27 Global AI Automation Testing Market Outlook, By Other Technologies (2021-2030) ($MN)
  • Table 28 Global AI Automation Testing Market Outlook, By Application (2021-2030) ($MN)
  • Table 29 Global AI Automation Testing Market Outlook, By Web-Based (2021-2030) ($MN)
  • Table 30 Global AI Automation Testing Market Outlook, By Mobile-Based (2021-2030) ($MN)
  • Table 31 Global AI Automation Testing Market Outlook, By End User (2021-2030) ($MN)
  • Table 32 Global AI Automation Testing Market Outlook, By IT & Telecommunication (2021-2030) ($MN)
  • Table 33 Global AI Automation Testing Market Outlook, By Healthcare (2021-2030) ($MN)
  • Table 34 Global AI Automation Testing Market Outlook, By BFSI (2021-2030) ($MN)
  • Table 35 Global AI Automation Testing Market Outlook, By Government (2021-2030) ($MN)
  • Table 36 Global AI Automation Testing Market Outlook, By Defense And Aerospace (2021-2030) ($MN)
  • Table 37 Global AI Automation Testing Market Outlook, By Energy & Utilities (2021-2030) ($MN)
  • Table 38 Global AI Automation Testing Market Outlook, By Other End Users (2021-2030) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.