PUBLISHER: Grand View Research | PRODUCT CODE: 1301270
PUBLISHER: Grand View Research | PRODUCT CODE: 1301270
The global AI-enabled testing market size is anticipated to reach USD 1.63 billion by 2030, registering a CAGR of 18.4% from 2023 to 2030, according to a new report by Grand View Research, Inc. Emerging Artificial Intelligence (AI) is transforming the technological landscape. Its application in numerous facets of software and tools development is fueling market growth. Moreover, Significant innovations and launches in computer vision & image processing are anticipated to drive market growth. For instance, Applitools's Visual AI for software testing provides accuracy using trained AI and Machine Learning (ML) algorithms. Similarly, in April 2023, A no-code, generative AI-driven mobile app testing solution SofySense was introduced by the software testing platform Sofy.
By integrating GPT-integrated, intelligent software-testing AI technology, the service improves Sofy's current platform and aids in quality assurance.Major players are focusing on innovations and the development of AI-enabled testing tools to stay ahead in the market. For instance, in October 2022, Keysight Technologies unveiled new automated AI testing to enhance 5G devices. The business launched this testing tool to increase smartphone users' interaction with built-in applications through automation and artificial intelligence. Similarly, in February 2021, Sauce Labs Inc. acquired AutonomIQ; This acquisition enhances scriptless test automation to an expanding capability set that already includes visual testing, API testing, front-end performance testing, and functional UI testing to broaden further and deepen the Sauce Labs portfolio.
The self-healing test is likely to fuel the AI-enabled testing tools market growth. The components of an application are defined by distinct identifiers in traditional test automation systems. These elements may also alter whenever the application is upgraded or modified. Since items have fixed definitions, previously successful tests will now fail and produce false negative results. AI algorithms are used in self-healing tests to detect unanticipated mistakes caused by dynamic properties automatically and either suggest a better solution or automatically update the script. This testing avoids test failures and saves QA staff time from having to track down and resolve any problems.