10 AI Testing Tools to Streamline Your QA Process in 2024

Artificial Intelligence (AI) may seem like a recent innovation, but its origins date back to the early 1900s when Leonardo Torres Quevedo developed “El Ajedrecista” — an automaton for chess endgames. Today, companies across various industries incorporate AI and machine learning (ML) for applications such as sales, email marketing, business operations, and conversational AI.

AI has also become an essential component of the software development process. Our 2023 Currents report, which captured the sentiments of respondents from domains like developer tools, tech infrastructure, fintech, blockchain, gaming, and entertainment, reveals that 47% use AI/ML in software coding.

AI can be impactful from the initial stages of planning and designing to coding and testing. In the planning and design phases, AI assists by analyzing vast amounts of data to identify trends, user preferences, and potential risks. When it comes to coding, AI helps by offering intelligent code completion, bug detection, and automated code generation. One of the most significant transformations AI brings is in the testing phase. AI-driven testing tools streamline your quality assurance (QA) process by automating repetitive tasks, executing parallel tests, and analyzing test results quickly and accurately. In this article, we discuss the benefits of AI testing tools and explore the top ten applications to consider for your software development team.

Summary

1. AI software testing tools support automated test case generation, self-healing capabilities, and predictive analytics, accelerating the development lifecycle.

2. These tools streamline QA processes, ensuring comprehensive test coverage, faster issue resolution, and improved software reliability, which is crucial for maintaining high-quality user experiences.

3. AI testing tools provide functional testing, visual validation, performance testing, and integration with CI/CD pipelines, catering to diverse testing needs across web and mobile applications.

What are AI testing tools?

AI testing tools are advanced software solutions that combine artificial intelligence and machine learning technologies to improve and automate various aspects of the software testing process. These tools aim to improve testing activities’ efficiency, accuracy, and coverage by using AI algorithms.

Benefits of AI testing tools

AI software testing tools use machine learning algorithms and generative AI techniques to transform software testing practices. By automating test case generation, maintaining scripts with self-healing capabilities, and providing predictive analysis for issue detection, they streamline the testing process.

Improved test automation and efficiency

AI testing tools boost the efficiency of test automation by automatically generating test cases and maintaining test scripts. For example, AI-driven test automation tools in a cloud-based CRM application can analyze user interactions and generate relevant test cases, ensuring comprehensive test coverage without manual effort. This streamlines the testing process, allowing QA teams to focus on more critical tasks, ultimately accelerating the software development lifecycle.

Improved test maintenance with self-healing capabilities

One of the standout benefits of AI test automation tools is their ability to maintain test scripts through self-healing capabilities. When a cloud-based e-commerce platform undergoes frequent updates, AI-powered testing tools can automatically adapt to UI changes and update the test scripts. This reduces the need for manual script maintenance, ensuring that automated tests remain functional and reliable even as the application evolves.

Predictive and path analysis for proactive issue detection

AI testing tools excel in predictive and path analysis, helping to identify potential issues before they impact users. For example, in a cloud project management application, AI can analyze historical data and current test results to predict areas that are likely to fail. This allows quality assurance teams to focus their testing efforts on high-risk areas, improving the overall stability and performance of the application. The result is a stronger and more reliable product for end-users.

Visual testing and analytics for comprehensive UI validation

Visual AI capabilities in testing tools enable thorough UI validation across various devices and screen sizes. For instance, a content management system (CMS) on the cloud can use AI-powered visual testing tools to compare visual elements and layouts. This ensures consistency and correctness of the user interface, providing a seamless user experience across desktop and mobile browsers. The visual test analytics offered by these tools give detailed insights into UI issues, supporting quicker resolution.

Continuous testing and smooth integration in CI/CD pipelines

AI-powered software testing tools are designed for integration into CI/CD pipelines, facilitating continuous testing. For a cloud-based enterprise resource planning (ERP) system, these tools could enable automated tests to be executed with every code change, providing immediate feedback on test results. This continuous testing approach ensures that errors are caught and addressed early in the development process, improving the overall quality and reliability of the software.

10 AI testing tools

AI-powered software testing tools are designed to strengthen test automation and efficiency across various testing needs. Each tool specializes in different aspects of software testing, from functional and regression testing to performance and usability testing, catering to everything from agile development teams to enterprise environments’ requirements.

1. Testim

Testim is an AI-powered test automation tool designed to accelerate the creation and maintenance of automated tests. It uses generative AI and machine learning algorithms to generate, execute, and maintain test cases, making it ideal for web-based software and mobile applications. Testim is particularly useful in environments requiring rapid test creation and maintenance, such as agile development teams.

Key features:

• Flexible testing environments that adapt to various testing needs by providing versatile environments for executing tests.

• Conducts web tests across various browsers to ensure compatibility and performance through Selenium-compatible cross-browser web tests

• Execute tests on both physical and virtual mobile devices to ensure comprehensive mobile app quality.

• Integrate with Continuous Integration/Continuous Deployment processes to automate tests on code check-ins.

• Salesforce testing, end-to-end testing designed for businesses using Salesforce without coding.

• Supports desktop and mobile browsers.

2. Applitools

Applitools specializes in visual AI testing and offers advanced capabilities for visually validating user interfaces. It is widely used in applications that require a consistent user interface (UI) across various devices, such as e-commerce and financial services platforms. Applitools ensures that visual elements are accurate and consistent, improving user experience.

Key features:

• Enterprise-proven AI implements a model trained with a decade of experience with proprietary algorithms used by growing companies.

• Creates tests using codeless tools, in the preferred framework, or AI-generated tests.

• Test any interface and validate any user interface, including apps, components, and PDFs on screen or device.

• UI component testing to detect regressions and changes in Storybook components immediately with automated visual and functional testing.

3. Functionize

Functionize is an AI-powered testing platform that automates the entire QA lifecycle, from test creation to execution and maintenance, using generative AI techniques. It is suitable for web-based applications and integrates well with continuous testing environments. Functionize uses machine learning to understand and adapt to changes, reducing the need for manual test updates.

Key features

• Architect — a Chrome plugin that uses machine learning to create and maintain tests by recording user interactions and adapting to site changes, ensuring minimal maintenance.

• TestAGENTS — AI-powered systems that generate tests from real user journeys, auto-debug tests with root-cause analysis, and create test plans for case management tools.

• Integrates visual verifications into automated tests using computer vision and AI to ensure pixel-perfect user experiences.

• Adapts for minor browser variations provide detailed visual comparisons and completion models and democratize testing by making it accessible to the entire team.

• Natural language processing for test creation.

4. Mabl

Mabl is an AI-powered test automation tool that focuses on making automated testing accessible to all development team members. It is particularly effective for agile teams working on web applications. Mabl uses machine learning to detect and adapt to application changes, ensuring test coverage across a wide spectrum.

Key Features:

• Creates browser UI tests for the user journey, including Scalable Vector Graphics (SVGs), Two-Factor Authentication (2FA), email, and PDFs, using low-code and JavaScript.

• Prepares mobile UI tests for hybrid or native mobile frameworks using low-code and Appium scripts.

• Integrates API tests into web and mobile tests using Mabl’s code editor or import directly from Postman collections.

• Streamlines web accessibility testing and fixes web accessibility issues by reusing existing browser UI tests.

• Performs efficient browser and API load testing by reusing browser and API tests with Service Level Agreements (SLAs) for core web vitals and user flow duration.

5. Test.ai

Test.ai products provide AI-powered testing solutions to automate functional and regression testing for mobile and web applications. It uses machine learning to create and maintain tests, reducing manual testing efforts and increasing test accuracy. Test.ai is ideal for applications with frequent updates and complex user interactions.

Key features:

• AI-driven functional and regression testing

• Low-code test automation solutions that cater to agile workflows and fast-paced release cycles.

• Test automation platform integrates accessibility testing into existing UI tests to identify and resolve accessibility issues early in development. This ensures a more inclusive user experience and higher overall application quality.

• Unified browser and API functional testing platform, where teams can deliver software updates frequently (daily, weekly, or monthly) without compromising on testing thoroughness.

• Integrates with existing tools and workflows, cultivating collaboration among team members and ensuring the reliability of automated tests throughout the development lifecycle.

6. Sauce Labs

Sauce Labs is a comprehensive test automation platform with AI-powered features for optimizing test execution and maintenance. It supports a wide range of testing needs, including cross-browser testing, mobile testing, and load testing. Sauce Labs is well-suited for large-scale enterprise applications requiring extensive test coverage.

Key Features:

• Simplifies test case creation and execution using AI-driven SaaS testing capabilities to streamline testing processes efficiently.

• Ensures high API quality throughout the development lifecycle with early feedback and validation, improving reliability and functionality.

• Performs visual testing to ensure applications meet design specifications and deliver visually appealing experiences across platforms and devices.

• Creates reports that identify and resolve errors before they impact users, using error debugging tools for faster issue resolution.

7. Tricentis Tosca

Tricentis Tosca combines AI and machine learning algorithms to automate the testing process across various application layers, including web, mobile, and API testing. It is widely used in enterprise environments where comprehensive test coverage and integration with business processes are prioritized.

Key Features:

• Tosca Copilot — A generative AI assistant that uses advanced Large language models (LLM) to automate test processes and optimize test assets. It supports the testing lifecycle from planning to execution, fastening the onboarding process and reducing redundancy in testing efforts.

• Automates repetitive tasks and provides intelligent insights for test optimization.

• Vision AI — Mimics human visual perception to automate testing for complex applications, including those on remote desktops, enhancing test coverage.

• Generates test automation directly from design mockups, allowing for testing early in the development process before code implementation.

• Enables smarter “go/no-go” release decisions by aligning testing efforts with business priorities and risk factors, ensuring more reliable software deployments.

8. TestCraft

TestCraft is an AI-powered test automation platform that focuses on providing a low-code solution for creating and maintaining automated tests. It is particularly useful for teams with limited coding skills who need to test web-based applications. TestCraft’s AI capabilities ensure that test scripts are updated automatically, reducing manual maintenance efforts.

Key features:

• Automatically generates tests across major automation frameworks and programming languages, improving test coverage and efficiency.

• Generates creative and comprehensive test scenarios using AI, ensuring thorough test coverage without overlooking critical scenarios.

• Identifies and addresses accessibility issues within projects, offering actionable suggestions to improve usability and compliance with accessibility standards.

• Streamlines testing processes by converting AI-generated test ideas into automated tests, optimizing testing workflows, and improving productivity.

9. Keysight Eggplant Test

Keysight Eggplant Test is an AI-driven test automation tool offering a comprehensive functional, performance, and usability testing suite. It is suitable for complex applications that require end-to-end testing across various platforms and devices. Eggplant’s AI capabilities help in creating tests that adapt to application changes.

Key features:

• Integrates with popular CI/CD tools like Jenkins, Azure DevOps, and GitHub Actions, facilitating automated testing workflows from code check-ins to deployments.

• Supports testing across diverse environments, including browsers, operating systems, and devices, ensuring consistent application performance and user experience across platforms.

• Simplifies setup of deployment tools for automated testing, eliminating the need for custom coding and complex configurations, thereby accelerating test setup and deployment times.

• Automates regression tests, improving testing efficiency and freeing resources for more strategic testing efforts.

• Uses AI algorithms for intelligent test automation, optimizing testing accuracy and scalability, which is particularly beneficial for testing responsive user experiences and complex tech ecosystems.

10. Perfecto

Perfecto is an AI-powered test automation platform designed to test mobile and web applications across multiple devices and browsers. It provides comprehensive testing capabilities, including functional, performance, and visual testing, making it ideal for organizations that deliver high-quality user experiences.

Key features:

• Conducts performance testing at scale using open-source tools like Apache JMeter™, Selenium, Gatling, Grinder, and Locust across the web and mobile apps, microservices, and APIs.

• Integrates Android emulators and iOS simulators into testing strategies, expanding test coverage early in the development lifecycle while minimizing escaped defects.

• Combines virtual and real devices within a single platform, facilitating faster and more thorough testing of applications across various device types and operating systems.

• Offers UX load testing for web and mobile devices, simulating real-world conditions in the cloud with scalability of up to two million virtual users. This ensures comprehensive testing of application user experience and backend performance under load, improving reliability and efficiency.

• Performs API testing and monitors API behavior from early development to production phases. It supports powerful assertions to verify data accuracy, facilitates testing of diverse scenarios, and integrates performance with functional testing for comprehensive API testing and monitoring throughout the development lifecycle.

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