| Market revenue in 2023 | USD 498.4 million |
| Market revenue in 2030 | USD 1,627.2 million |
| Growth rate | 18.4% (CAGR from 2023 to 2030) |
| Largest segment | Solution |
| Fastest growing segment | Services |
| Historical data covered | 2017 - 2022 |
| Base year for estimation | 2023 |
| Forecast period covered | 2024 - 2030 |
| Quantitative units | Revenue in USD million |
| Market segmentation | Solution, Services |
AI-enabled testing is applying artificial intelligence (AI) techniques in software testing. It involves using AI algorithms, machine learning (ML) models, and other AI techniques to improve the efficiency, effectiveness, and accuracy of software testing processes. The emerging use of ML in testing for making automated tests more resilient and less brittle is propelling market growth.
Machine learning (ML) makes the current automated tests more resilient and brittle. Moreover, ML models learn from existing test data to identify patterns and behaviors that indicate correct or incorrect software behavior. These models are then used as oracles to validate the correctness of new test runs augmenting the market growth.
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| Name | Profile | # Employees | HQ | Website |
|---|---|---|---|---|
| Capgemini SE | View profile | 340,400 | 11 rue de Tilsitt, Paris, France, 75017 | www.capgemini.com |
| D2L Inc (Sub Voting) | View profile | 1,000 | 137 Glasgow Street, Suite 560, Kitchener, ON, Canada, N2G 4X8 | www.d2l.com |
| Micro Focus | View profile | 10001+ | Newbury, West Berkshire, United Kingdom, Europe | www.microfocus.com |
| Sauce Labs | View profile | 501-1000 | San Francisco, California, United States, North America | saucelabs.com |
| retest | View profile | 11-50 | Karlsruhe, Baden-Wurttemberg, Germany, Europe | retest.de/ |
| Functionize | View profile | 11-50 | Walnut Creek, California, United States, North America | www.functionize.com |
| Diffblue | View profile | 11-50 | Oxford, Oxfordshire, United Kingdom, Europe | www.diffblue.com/ |
| Applitools | View profile | 101-250 | Tel Aviv, Tel Aviv, Israel, Asia | applitools.com/ |
| testRigor | View profile | 51-100 | San Francisco, California, United States, North America | testrigor.com |
| Tricentis | View profile | 1001-5000 | Austin, Texas, United States, North America | tricentis.com |
The databook is designed to serve as a comprehensive guide to navigating this sector. The databook focuses on market statistics denoted in the form of revenue and y-o-y growth and CAGR across the globe and regions. A detailed competitive and opportunity analyses related to AI Enabled Testing Market Outlook will help companies and investors design strategic landscapes.
Solution was the largest segment with a revenue share of 77.05% in 2023.
Horizon Databook has segmented the Global ai enabled testing market based on solution, services covering the revenue growth of each sub-segment from 2017 to 2030.
AI-enabled testing is applying artificial intelligence (AI) techniques in software testing. It involves using AI algorithms, machine learning (ML) models, and other AI techniques to improve the efficiency, effectiveness, and accuracy of software testing processes. The emerging use of ML in testing for making automated tests more resilient and less brittle is propelling market growth.
Machine learning (ML) makes the current automated tests more resilient and brittle. Moreover, ML models learn from existing test data to identify patterns and behaviors that indicate correct or incorrect software behavior. These models are then used as oracles to validate the correctness of new test runs augmenting the market growth.
Horizon Databook provides a detailed overview of global-level data and insights on the Global AI Enabled Testing Market Outlook, including forecasts for subscribers. This global databook contains high-level insights into Global AI Enabled Testing Market Outlook from 2017 to 2030, including revenue numbers, major trends, and company profiles.
