Magic Quadrant for AI-Augmented Software Testing Tools

How does Gartner define the AI-Augmented Software Testing Tools market in 2025?

Gartner defines AI-augmented software testing tools as tools that provide fully integrated and orchestrated capabilities to enable continuous, self-optimizing and highly autonomous testing in the software development life cycle (SDLC) through the use of AI. Capabilities include the generation and maintenance of test scenarios, test cases, test automation, test suite optimization, test prioritization, test analysis, and test value scoring. As part of the larger toolset for AI-augmented development that aids software engineers in designing, coding and testing applications, AI-augmented software testing tools integrate with AI code assistants, chat interfaces, DevOps platforms, planning and deployment tools. They are delivered primarily as cloud-hosted services with some options for on-premises deployment.

Key Facts for Magic Quadrant for AI-Augmented Software Testing Tools in 2025

Publication Date: 6 October 2025

Document ID: G00828088

Coverage: Global

Authors: Joachim Herschmann, Sushant Singhal, Ross Power, C.A. Swan

Core Purpose: AI-augmented software testing tools are context-aware, data-driven and increasingly autonomous tools that enable software engineering leaders to deliver higher-quality products faster. Use our evaluation of AI-augmented software testing vendors to select the best fit for your organization.

How was the AI-Augmented Software Testing Tools market evolved in 2025?

Gartner forecasts that in 2025, spending on testing tools will reach $2.8 billion

By 2028, the market is expected to reach $3.3 billion, growing at a CAGR of 5.3% between 2022 and 2028

AI-augmented software testing tools provide fully integrated and orchestrated capabilities to enable continuous, self-optimizing and highly autonomous testing

The market is evolving from GenAI assistants to agentic AI capabilities, making testing processes increasingly autonomous

Vendors are pursuing agent-based ecosystems and Model Context Protocol integration

The market has seen significant M&A activity since 2024, including BrowserStack acquiring Requestly, SmartBear acquiring Reflect and QMetry, and Tricentis acquiring SeaLights

Tools are primarily delivered as cloud-hosted services with some on-premises deployment options

Leaders in this market serve large enterprises across finance, healthcare, insurance, public sector, and technology industries

What product features are required to be included in this year's evaluation?

Conversational user interfaces: Support for natural language and prompt-based interactions for the purpose of fulfilling a request, such as asking questions, creating test artifacts or completing a task.

GenAI for test development: Support for generative AI (GenAI) and large language models (LLMs) that can automatically generate a set of test artifacts, including test plans, test cases and test automation scripts. Data sources for training these models typically include large repositories of original source content such as technical documentation, requirements documents, code repositories, test descriptions or log files of real user interactions. Additional capabilities include providing suggestions for improving existing artifacts.

What are the common features of top products in the AI-Augmented Software Testing Tools space?

Native automated UI, API and visual testing capabilities: Support for automated testing of web and native mobile applications through the UI, the API and services interfaces via integrated, native capabilities. These capabilities also support visual testing capabilities that highlight crucial changes to an application's layout and/or content that breaks the user experience.

Self-healing for test scripts: Automatic root cause analysis for failed test cases and recommendation of a fix (minimum capability) or automatic refactoring of the test case to fix the issue.

Integrations: Support for integrations with DevOps platforms, planning tools, version control systems, data and infrastructure platforms, reporting tools and container tools for efficient regression testing and testing across environments.

Team collaboration: Support for the visualization of testing workflows, a built-in knowledge base supporting the sharing of information and best practices and real-time communication through integrated chat or messaging interfaces. These capabilities can be either built into the tool or offered via seamless integrations with the customer's existing platform.

Enterprise administration: Support for single sign-on (SSO), role-based access control (RBAC), multifactor authentication (MFA), centralized user management, and the ability to support a large number of users and transactions as the organization grows.

Model management: Support for different AI models for optimized software testing, including out-of-the-box (vendor-provided) models, models provided by third-party vendors, open-source models and a bring-your-own-model (BYOM) option.

Agentic AI: Support for goal-driven software entities that have been granted rights by the user to act on the user's behalf to autonomously make decisions and take action. These agents use AI techniques — combined with components such as memory, planning, sensing, tooling and guardrails — to complete tasks and achieve objectives.

Manual to automated test conversion: Generation of automated tests for a range of different automation tools and frameworks by analyzing manual test case descriptions already captured in office documents, test management tools or other means of documentation, or by observing real user interactions. Users get access to the generated code as well to allow for customizations and migrations if needed.

GenAI application testing: Support for testing of GenAI-powered applications exhibiting probabilistic behavior such as chatbots, LLM-powered conversational experiences and autonomous agents — testing both latency and quality of output.

Test framework support: Support for the import, export and generation of test automation code for multiple testing frameworks (such as Selenium, Appium and Cucumber) in addition to the proprietary vendor ecosystem.

Performance testing: Support for front-end page load (waterfall chart) measurement and full-scale back-end load testing.

Test orchestration and prioritization: Support for prioritizing, optimizing and parallelizing test execution based on criteria such as reliability (flakiness) of tests, code changes or updates in test environments (change impact analysis).

Defect prediction: Identification of gaps in quality and defect targets, minimization of redundancy, and improvement of the effectiveness and efficiency of testing processes by detecting patterns in historical quality assurance (QA) data.

Service virtualization/API testing: Support for shift-left testing through the ability to test APIs and create virtual orchestrated services (not just simple mocking) instead of production services.

Test data generation: Generation of synthetic test data that retain the structure and statistical properties (like correlations) of production data without a one-on-one relationship to the original data.

Dashboard: An extensible and configurable (through templates or a conversational user interface) web dashboard that provides teams visibility into the overall test process. This includes views for the quality of software components, interdependencies between services, connected environments and drill-down options to view individual test results. The dashboard is customizable, enabling information curation by individuals and teams, and is extensible via plug-ins, webhooks and custom apps.

Marketplace: Facilitation of the exchange of skills and knowledge, enabling the discovery of shared test repositories, and providing a curated collection of approved tools and libraries.

Accessibility: Automated scanning of UI against recognized international standards (e.g., Web Content Accessibility Guidelines [WCAG] from W3C).

Migration capabilities: User migration onto and away from the product in case the user wants to change vendors without losing their data and wasting effort rebuilding what they created using existing tools to avoid potential lock-in.

Scope Exclusions

Tools primarily for testing low-code applications, packaged business applications or SaaS-based applications (e.g., Salesforce, Microsoft Dynamics 365, Oracle, SAP, ServiceNow)

Tools targeting only a single system platform (only web, only mobile, or only desktop)

Platforms only sold as part of custom software development or professional services engagements

Inclusion Criteria

Vendors must, among other requirements:

Provide a dedicated, generally available (GA) AI-augmented software testing tool with public pricing

Sell the solution directly to paying customers without requiring professional services

Demonstrate an active product roadmap, go-to-market and selling strategy

Have phone, email and web customer support in English

Have at least 10% of paying customers in each of three of four geographic regions (US/Canada, Central/South America, Europe, Asia/Pacific)

Have sales or partner network presence spanning at least three regions

Offer native support for conversational user interfaces

Offer native support for GenAI for test development

Offer native automated UI, API and visual testing capabilities

Offer self-healing for test scripts

Support integrations with DevOps platforms and development tools

Support team collaboration features

Support enterprise administration (SSO, RBAC, MFA)

Generate at least $30 million in annual GAAP revenue in 2024 with at least 200 paying enterprise customers OR $25 million with 40% YoY growth or 50 net-new enterprise logos

Score at least 44 in the Customer Interest Indicator (CII)

Ability to Execute — Relative Weighting

Product or Service - High

Overall Viability - High

Sales Execution/Pricing - Medium

Market Responsiveness/Record - Medium

Marketing Execution - Medium

Customer Experience - High

Operations - Low

Completeness of Vision — Relative Weighting

Market Understanding - High

Marketing Strategy - Medium

Sales Strategy - Medium

Offering (Product) Strategy - Medium

Business Model - Medium

Vertical/Industry Strategy - Low

Innovation - High

Geographic Strategy - Medium

FAQs

Q: What does this research cover?

A: This research evaluates 11 vendors of AI-augmented software testing tools across two key dimensions: Ability to Execute and Completeness of Vision. It covers vendors that provide dedicated, generally available AI-augmented software testing tools with native support for conversational interfaces, GenAI for test development, automated UI/API/visual testing, self-healing, integrations, team collaboration, and enterprise administration. The evaluation includes mandatory and common features, vendor strengths and cautions, market dynamics, and strategic recommendations for selecting tools based on organizational needs.

Q: Who should use this research?

A: This research should be used by software engineering leaders and their teams who are evaluating, selecting, or implementing AI-augmented software testing tools. It is particularly valuable for organizations looking to: (1) understand the competitive landscape and vendor positioning, (2) identify vendors that align with their specific testing requirements and strategic direction, (3) assess vendor capabilities across product features, viability, pricing, customer experience, and innovation, (4) develop a future-proof testing strategy that incorporates AI and agentic capabilities, and (5) make informed decisions about tool selection based on their organization's size, industry, geography, technical requirements, and budget constraints.

Q: What are the mandatory features of vendors included in this market?

A: Vendors must offer native support for: (1) Conversational user interfaces with natural language and prompt-based interactions, (2) GenAI for test development using large language models to automatically generate test artifacts including test plans, test cases and test automation scripts, (3) Native automated UI, API and visual testing capabilities for web and mobile applications, (4) Self-healing for test scripts with automatic root cause analysis and fix recommendations, (5) Integrations with DevOps platforms, planning tools, and version control systems, (6) Team collaboration features including workflow visualization and knowledge sharing, and (7) Enterprise administration including SSO, RBAC, MFA and centralized user management.

Q: What are some reasons for not being included in this report?

Primary use case is testing low-code applications, packaged business applications, or SaaS-based applications (e.g., Salesforce, SAP, ServiceNow customizations)
Target is only a single system platform (web-only, mobile-only, or desktop-only)
Platform is only sold as part of custom software development or professional services engagements
Does not meet minimum revenue requirements ($30M annual revenue with 200+ enterprise customers, or $25M with 40% YoY growth/50 new logos)
Does not meet geographic presence requirements (10% of customers in 3 of 4 regions)
Does not offer mandatory technical capabilities (conversational UI, GenAI test development, native UI/API/visual testing, self-healing, integrations, collaboration, enterprise administration)
Customer Interest Indicator (CII) score below 44

Q: What differentiates Ability to Execute vs. Completeness of Vision?

A: Ability to Execute evaluates a vendor's current market performance, including product quality, financial viability, sales effectiveness, market responsiveness, marketing reach, customer satisfaction, and operational excellence. It focuses on present capabilities and execution strength. Completeness of Vision assesses a vendor's strategic direction and future potential, including market understanding, product roadmap, innovation strategy, business model soundness, vertical/industry focus, geographic expansion plans, and marketing/sales strategies. It focuses on the vendor's ability to anticipate and shape future market needs and trends.

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