Top Five AI-Powered Tools for Test Automation

AI development is a highly debated topic. Some people have serious concerns about it, while others are excited about the thrilling opportunities it offers. Anyway, AI technologies have already been incorporated across different fields and started to transform them. Quality insurance is no exception.

Currently, there are a lot of AI-based testing tools on the market. How can they help you? Which one to choose? If you do not know the answers to these questions, stick around. We have selected the top 5 AI-based automation testing tools, taking into account the opinions of some experts represented in the list of top software testing companies.

What Are AI-Powered Testing Tools?

AI-powered testing tools are software solutions that leverage artificial intelligence and machine learning algorithms to automate and streamline diverse aspects of software testing. 

Empowered by AI, they significantly simplify test writing and maintenance, test data generation, and test execution, ultimately taking software testing to the next level. In essence, AI-driven tools immensely simplify the work of QA teams by offering an array of compelling benefits, including the following ones:

Better Test Result Accuracy

AI-driven tools excel in delivering precise and consistent test results, decreasing the number of false positives and negatives.

Advanced Bug Tracing

These tools are good at identifying and tracing bugs, helping to pinpoint issues swiftly and efficiently.

Predictive Analysis

Predictive capabilities of AI-based testing tools enable smart test effort prioritization, allowing QA specialists to concentrate on critical areas thanks to data-driven insights.

Codeless Script Creation

With a user-friendly, codeless approach, AI-based tools offer a low learning curve, empowering testers with varied technical backgrounds to create tests effortlessly.

Minimal Test Maintenance

Amazing capabilities of tests for self-healing ensure minimum or even zero test maintenance efforts, even as software evolves.

Increased Test Coverage

AI-driven testing extends test coverage, mitigating the risk of missing essential test scenarios.

Accelerated Automation

These tools immensely speed up all aspects of test automation, leading to shorter time-to-market.

If you haven't yet explored the potential of AI-driven test automation tools, now is an ideal moment to consider their adoption. Our list of the best AI automation testing tools can provide invaluable assistance in selecting the right tool for your specific testing needs.

Top AI-Based Automation Testing Tools

1. Perfecto Scriptless Mobile

This is a scriptless Selenium-based test solution that gives an opportunity to create stable automated tests for complex mobile applications in the blink of an eye. Implemented AI algorithms obviate the need for manual maintenance of tests.

Pros:

• Super quick and simple native mobile testing;
• An opportunity to engage anyone on the team in testing;
• No need to set up a framework or infrastructure;
• No need for coding skills;
• Conditional logic, checkpoints, grouping, looping, and other functionalities are supported;
• Scriptless Test and Playback function is available;
• Automated detection of changes in the AUT and adaptation to them.
• Comprehensive test reports.

Cons:

• Limited opportunities for advanced customization;
• It’s a paid tool, but a free trial is available;
• Dependency on the vendor in terms of updates, support, and maintenance.

Price: Four pricing packages are available. You can choose between Live (USD 83 per month), Automation (USD 123 per month), Scriptless(on request), and Enterprise (on request).

2. Applitools

Applitools is a ground-breaking software testing solution driven by Visual AI. It is trusted by leading world brands like Microsoft, Adidas, Salesforce, Dow Jones, and others. Using the power of Artificial intelligence greatly shortens the time needed for test writing, performance, and maintenance.

Pros:

• Scriptless test creation, run, maintenance, and reporting;
• An AI-driven visual validator that enables quick and accurate automated UI testing;
• Self-healed test infrastructure;
• Quick cross-browser and cross-device testing;
• Grouping of bugs with similar traits;
• Root cause analysis;
• Autotest maintenance;
• Support of diverse types of testing.

Cons:

• It’s a commercial solution whose price varies based on usage and the number of test runs;
• Applitools mainly concentrates on visual testing, and there may be a need for additional tools to cover a whole scope of testing needs;
• In spite of using AI for the reduction of false positives in visual comparisons, sometimes there are instances of flagging non-critical differences.

Price: Applitools provides three price packages — Starter, Eyes, and Ultrafast Test Cloud. Contact the Applitools team for more details.

3. Functionize

If you are looking for a next-gen test automation solution, this is it. Functionize is a modern cloud-based software testing solution that harnesses the power of Big Data, ML, and computer vision.

Pros:

• Completely autonomous ML-based test generation;
• Smooth integration into DevOps and a CI/CD pipeline;
• Self-healed e2e tests that quickly adapt to changes in a website;
• Ability to test not only the UI/UX of web apps but also DBs, PDF files, emails, etc.
• Test execution on the cloud without a need to maintain infrastructure;
• Ability to write your own logic by means of custom code and extensions.

Cons:

• Lack of stability;
• A range of features is available only in the most expensive package;
• A bit complex setup requiring knowledge of how AI-based testing tools work in general;
• Test Architect verifies only HTML-based websites.

Price: The data is not publicly available but you can request a quote on the official website.

4. AccelQ

Let us introduce one more next-gen cloud-based test automation solution that leverages the power of artificial intelligence. As well as the mentioned above tools, it is scriptless. What makes AccelQ special is its support of web, mobile, desktop, API, and backend testing.

Pros:

• Ability to develop complex test scripts by using plain, natural English;
• Capabilities for parallel test runs;
• Smooth integration into a CI/CD pipeline;
• The platform is completely extendible;
• Inbuilt version control solution;
• Reusable test components help to save a lot of time;
• Self-healed tests;
• Test data generation tool.

Cons:

• Test Result Reporting has some areas of improvement;
• Difficulties with agent management in the long run;
• A quite high price that not all small companies can afford.

Price: Four packages are available with regard to your needs: Automate Web, Automate Mobile, Automate API, and Automate Manual. Pricing data is provided on request.

5. Testim

Are you looking for a full-featured test automation platform? Here it is. Testim is a great AI-powered solution that enables the easy and fast creation of E2E tests for custom web apps.

Pros:

• An opportunity to customize tests via Visual Editor without code;
• An ability to insert your own custom code into any test step and run it inside and outside browsers;
• An opportunity to add, group, and reuse test steps;
• Stable tests requiring minimum maintenance effort thanks to AI-driven smart locators;
• Recurring issues are grouped by type helping address frequent problems first;
• Cross-browser tests can be run on the Testim grid or any grid that is compatible with Selenium;
• High-quality visuals and relevant data enable quick troubleshooting;
• Integration with dev tools and CI tools.

Cons:

• Inability to use tests of one project in another one;
• Lack of features for compliance testing;
• The primary focus is on test automation for web apps;
• While the tool has an intuitive UI, working with some functionality may be a bit confusing.

Price: Four pricing packages are available – Community (free of charge), Essentials (USD 450 per month), Pro (USD 1000), and Mobile (USD 90 per month).

Key Features of AI-Driven Testing Tools

AI-driven testing tools offer a wide scope of helpful features that differentiate them from traditional testing solutions. Let’s review the most prominent ones.

AI-Powered Test Generation

Using efficient AI, ML, and NLP models, such tools collect and meticulously analyze data from different sources like historical data, requirements, app code, UI, user interactions, etc., and leverage it to generate test scripts or test cases automatically. This significantly accelerates the process of test creation.

Self-Healing

Any changes in software UI or code are identified automatically, and existing test scripts are updated correspondingly. This helps minimize the test maintenance effort.

Predictive Analytics

Implemented AI algorithms to carefully analyze historical test data to predict possible flaws and identify app areas with a high likelihood of having issues. This is important for the correct test prioritization.

Visual Testing

AI-driven tools have great capabilities for visual testing, comparing visual elements of an app UI across various devices and resolutions to identify any discrepancies.

Test Data Generation

Both synthetic and semi-synthetic test data can be generated on the ground of learned patterns. Moreover, this data may be of various types, such as text, numbers, dates, and images, with regard to the testing requirements. Some tools may also validate data uniqueness, referential integrity, quality, and other aspects.

Low-Code or Completely Codeless Testing

As a rule, most AI-based tools offer low-code or codeless test creation via intuitive interfaces, making it possible to engage non-technical team members in test automation.

Advanced Bug Detection

AI algorithms are utilized to catch subtle defects or abnormalities in the app behavior that hardly can be identified through manual testing.

Reporting and Analytics

Ai-based testing tools provide comprehensive reporting and rich analytics, letting teams gain valuable insights, recognize patterns, and make informed decisions.

Test Case Prioritization

Test cases can be automatically prioritized based on such facets as code changes, historical defect data, and risk analysis. This way, testing efforts can be focused on the most critical areas of an AUT.

Together, these key features considerably improve and streamline software testing, making AI-powered testing tools a must for companies striving to deliver top-notch software solutions.

Bottom Line

Obviously, AI-based tools for test automation have truly transformed the QA sector, offering a plethora of benefits. Thus, they help to accelerate test automation, decrease manual effort, increase test coverage, and quickly adapt to the rapidly evolving nature of applications. All of these make them indispensable in the modern, fast-paced development landscape.

The future of AI-based test automation solutions promises to be even more exciting. We can definitely expect further advancements in AI algorithms, leading to smarter, more accurate, and adaptable test automation. Tools of this kind will likely become even more user-friendly, enabling an active engagement of a broader range of team members in testing activities. With these advancements, AI-based test automation tools will play a pivotal role in ensuring the reliability, scalability, and excellent quality of software in the increasingly digitalized world.