Ai-Enabled Testing Market Size, Share, Report 2023-2032
The global AI-enabled testing market was surpassed at USD 0.45 billion in 2022 and is expected to hit around USD 2.44 billion by 2032, growing at a CAGR of 18.44% from 2023 to 2032. The AI-enabled testing market in the United States was accounted for USD 124 million in 2022.
Key Pointers
Report Scope of the AI-enabled Testing Market
| Report Coverage | Details |
| Revenue Share of North America in 2022 | 40% |
| Revenue Forecast by 2032 | USD 2.44 billion |
| Growth Rate from 2023 to 2032 | CAGR of 18.44% |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Market Analysis (Terms Used) | Value (US$ Million/Billion) or (Volume/Units) |
| Companies Covered | Sauce Labs Inc.; ReTest GmbH; D2L Corp.; Functionize Inc.; Diffblue Ltd.; Applitools, Capgemini SE; testRigor; Micro FocusInternational Plc; Tricentis; Perforce Software Inc.; mabl Inc.; IBM Corp. |
Various major players are offering AI-enabled testing solutions; for instance, Google’s Deep Test is an AI-powered testing tool that uses Machine Learning (ML) to generate test cases and execute them on web applications automatically. It aims to improve test coverage and reduce the effort required to create test cases. Moreover, prominent innovations and developments in AI-enabled testing tools are fueling market growth. For instance, in May 2023, Parasoft released Jtest, a productivity tool for Java developers, and DTP in version 2023.
Parasoft Jtest with AI enhancements enhances code quality, lowers defect rates, and expedites software delivery. Significant innovations and launches in computer vision & image processing technology are anticipated to drive market growth. For instance, Applitools's visual AI for software testing technology provides accuracy using trained AI and ML algorithms. In April 2023, using text prompts, the vision AI company Chooch created Image Chat, a novel method for creating intricate computer vision models. The company introduced this Artificial Intelligence (AI) computer vision solution to enhance data dependability and accuracy, particularly in applications with object detection and precise reasoning requirements.
Small and Medium-sized Enterprises (SMEs) frequently employ AI testing services to evaluate the effectiveness of software programs. For SMEs, AI testing services offer many advantages, including lower costs, more efficiency, and improved user experience. SMEs have begun utilizing AI-based automation solutions, such as bots or other intelligent technology, to help their business activities throughout the execution phase rather than having to start from scratch each time.
Component Insights
The software segment held the highest revenue share of over 78% in 2022. SMEs frequently employ AI testing services to evaluate the effectiveness of software programs. For SMEs, AI testing services offer many advantages, including lower costs, more efficiency, and improved user experience. Moreover, organizations streamline preparing, managing, and analyzing test data by leveraging data-wrangling software in AI-enabled testing. This integration enhances testing accuracy, efficiency, and effectiveness by ensuring that the correct data is available in a suitable format, enabling comprehensive test coverage and reliable results. The service segment is estimated to grow at the highest CAGR over the forecast period.
The significant advancement of managed and professional services accounts for this growth. AI-enabled testing tools often requires significant computational resources and infrastructure to support complex algorithms and large-scale testing. Managed services providers like QA Mentor, Testlio, Capgemini, and others offer the necessary infrastructure, such as cloud-based platforms or dedicated environments, to support AI testing initiatives. They also scale resources based on demand, ensuring optimal performance, quality monitoring, and cost-efficiency; for instance, Capgemini’s embedded quality engineering in “ADMnext,” a whole stack of Application Development and Maintenance (ADM) services, have the capabilities and solutions to address a wide array of challenges and help the user make better business decisions ensuring better performance.
Deployment Insights
The on-premises segment led the market in 2022 accounting for over 62% of the global revenue. On-premises environments require the necessary infrastructure to support AI-enabled testing. This comprises setting up servers, storage, and networking capabilities to handle the computational requirements of AI algorithms and models. Organizations collect, store, and manage the relevant data required for AI-enabled testing within their on-premises infrastructure. Data collection involves ensuring data security, privacy, and compliance with regulations governing the handling of sensitive or confidential information. Moreover, organizations must customize or develop connectors, plugins, or interfaces to integrate AI capabilities into their existing testing tools, frameworks, or processes to ensure smooth collaboration and integration of AI techniques with the on-premises testing environment fueling the market growth.
The cloud segment is estimated to grow at the highest CAGR over the forecast period. This growth is led by cloud-based solutions offering virtually unlimited scalability and resources. Organizations efficiently deliver and scale up or down their AI-enabled testing infrastructure based on demand. This flexibility allows them to handle large-scale testing requirements efficiently and cost-effectively. Moreover, cloud-based AI-enabled testing tools can seamlessly integrate with other cloud services, such as cloud-based test management tools, version control systems, bug tracking systems, and continuous integration/continuous deployment (CI/CD) pipelines. This integration streamlines the software development and testing process, enhancing efficiency and collaboration.
Technology Insights
The machine learning & pattern recognition segment held the largest revenue share of over 48% in 2022. Al-enabled testing tools integrated with machine learning and pattern recognition technologies are augmenting the development of Self-Optimizing Networks (SON), which allow operators to automatically improve network quality based on traffic statistics by time zone and location; it is made possible by these cutting-edge solutions for Communication Service Providers (CSPs). Moreover, they employ cutting-edge algorithms to spot trends in the data, allowing telecom companies to identify and anticipate network flaws and avert problems before it negatively impacts customers.
The NLP segment is witnessing rapid growth owing to the acceptance of novel technology advancements. Increased customer demands for cloud-based technology and advancements in communication infrastructure are accelerating NLP growth. NLP is the interface between humans and machines, and it comprises executing computer programs and analyzing data. Low cost, high scalability, and high usage of smart devices across industries are expected to contribute to the industry expansion over the forecast period. Moreover, the expansion of the e-commerce industry and the perception of online sales channels are projected to increase the demand for NLP to enhance the consumer experience through personal attention and query handling.
Application Insights
The test automation segment held the largest revenue share of over 59% in 2022. The growth is led by AI-testing tools, which greatly enhance test automation by leveraging Artificial Intelligence(AI) algorithms and techniques. Combined with cloud computing instances, it can bring even more scalability and flexibility to the testing process. By combining AI-enabled testing with test automation, organizations achieve higher efficiency, accuracy, and productivity in their testing processes. AI algorithms also enhance test case generation, data management, test execution, analysis, and predictive capabilities, augmenting the capabilities of test automation tools and frameworks.
This ultimately leads to improved software quality, faster time-to-market, and better overall testing outcomes fueling the market growth. The infrastructure optimization segment is anticipated to showcase significant growth over the forecast period. Organizations leveraging AI techniques improve their testing infrastructure's efficiency, scalability, and cost-effectiveness. Rapid advancement in cloud infrastructure has enabled the IT Infrastructure to be flexible, intangible, and on-demand. AI algorithms automatically generate test cases based on the system's analysis under test, reduce the manual effort required for test case creation, and ensure comprehensive test coverage. Moreover, AI prioritizes test cases based on code changes, defect history, or criticality, allowing organizations to focus their testing efforts on high-priority areas.
End-Use Industry Insight
The IT & telecommunication segment held the largest revenue share of over 19% in 2022. The rising demand for efficient customer services and operating networks in the telecommunication industry are boosting market growth. AI testing has significantly impacted the telecommunication industry, bringing numerous benefits and advancements to various aspects of telecommunications. AI testing techniques automate the testing process, enabling faster and more efficient testing of telecommunication networks, services, and systems. AI algorithms generate test cases, execute tests, and analyze results at a scale, reducing the manual effort and time required for testing. It improves the efficiency of testing cycles and enables faster time-to-market for new telecom services and features. Moreover, the algorithm analyzes network traffic patterns, identifies anomalies, and detects potential security threats. This enables telecom operators to respond quickly to security breaches, detect and mitigate vulnerabilities, and ensure the integrity and privacy of customer data.
The BFSI segment is witnessing substantial growth and significant advancements in various testing areas. It plays a crucial role in fraud detection and prevention within the BFSI Industry. AI algorithms also analyze large volumes of transactional data, customer behavior patterns, and historical fraud cases to identify potentially fraudulent activities in real time. This helps financial institutions detect and prevent fraudulent transactions, minimizing financial losses and safeguarding customer assets. Moreover, AI-enabled testing aids financial institutions in assessing risks and ensuring compliance with regulatory requirements. AI algorithms analyze vast amounts of data, including transactional records, customer profiles, and regulatory guidelines, to identify potential compliance breaches or risks and assist in conducting comprehensive compliance testing and proactively mitigating potential risks.
Regional Insights
North America dominated the market in 2022, accounting for over 40% share of the global revenue. Prominent growth of automation testing is fueling the market growth in this region. AI regression testing is increasingly being used in mobile applications, influencing AI-enabled testing in North America as they improve the product's functionality. Moreover, the United States is expected to advance significantly over the forecast period due to technology providers' presence. Increasing urbanization, changing lifestyles, rising disposable income, and advanced technologies fuel the market growth in this region.
Increasing investment in R&D activities, increasing preference for automated testing solutions, and launching new products are also driving the market growth in the U.S. Asia Pacific is anticipated to witness significant CAGR growth over the forecast period. India, China, Japan, and other countries of Asia Pacific are innovating and launching new products and platforms to fuel market growth. Major innovations of 5G in Japan are propelling market growth in this region. The usage of AI-enabled testing technologies in Japan may increase as a result of a potential spike in demand for automated and efficient telecom infrastructure testing and maintenance. Moreover, In June 2022, Singapore introduced the first AIgovernance testing framework and toolbox to assist AI developers in objectively and independently evaluating their systems.
AI-enabled Testing Market Segmentations:
By Component
By Deployment
By Technology
By Application
By End-Use Industry
By Regional
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