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AI Automation Tools Enhancing Efficiency and Reliability in Software Testing

With AI pushing software testing into an era of enhanced efficiency, cost-effectiveness, and reliability, we present a quick introduction to AI-driven testing and a few popular tools for AI-powered test automation.


Test automation has evolved significantly over the years, reducing the potential for human errors within QA teams. The tools available today are more efficient, reliable, and user-friendly compared to the clunky and cumbersome options of the past. As a result, the discipline of test automation has made remarkable progress, embracing enhanced capabilities, features, seamless integrations, and robust frameworks. This advancement has also prompted a shift from the traditional waterfall model to the current landscape dominated by DevOps and Agile development principles.

In this article, we will explore the benefits of AI-driven tools for test automation and introduce some popular AI-powered automation testing tools available in the market.

Why AI-Driven Tools for Test Automation?

AI-driven tools for test automation are gaining popularity in the automation landscape due to the escalating complexity of enterprise applications and the increasing size of automation suites. Software development and testing teams are leaning towards automated checks to ensure that their software meets the desired quality standards set by the business and customers. The worldwide test automation market is projected to experience a substantial compound annual growth rate (CAGR) of 15.4% between 2017 and 2025, resulting in a market value of approximately US$ 109.69 billion by 2025.

One of the most compelling advantages of AI-powered tools is their ease of maintenance. These tools have self-healing functionalities that automatically rectify test scripts in response to changes in the application. This saves time and effort by automatically adjusting tests when new features are added or existing functionalities are enhanced.

Now, let’s explore some of the most popular AI-powered automation testing tools available in the market.

1. Testsigma

Testsigma is one of the foremost AI testing tools in the industry. It allows QA analysts and automation engineers to create automated tests for web, mobile, desktop applications, and APIs. Testsigma features auto-healing functionalities that seamlessly rectify errors. It also enables manual testers and team leaders to execute tests in parallel.

Key Attributes:
– Streamlined test development using natural language processing.
– Cloud-based architecture eliminates the need for installation.
– Test automation for web, mobile, desktop, and APIs.
– Commendable dashboard.
– Intuitive intelligence and automatic error correction.
– Data-driven functionalities for real-world test scenarios.
– Automated scheduling features and customizable report generation.
– Seamless integration with popular CI/CD tools.

Testsigma offers a free plan and Pro and Enterprise plans for subscription.

2. TestCraft

TestCraft by Perfecto is an AI-driven test automation tool built on the Selenium foundation. It supports both manual and automated testing and excels in delivering tests for web-based software. TestCraft can scale to meet the highest industry standards for quality and includes exceptional features for executing repetitive tests.

Key Attributes:
– Support for multiple programming languages.
– Copy-to-clipboard function for streamlined operations.
– Test idea generation tool leveraging advanced GPT technology.
– Integration capabilities with other tools.
– Available online as a Software as a Service (SaaS) solution.

TestCraft offers a range of subscription options, including a free trial, monthly, and yearly plans starting at $29 per user.


ACCELQ is a robust AI-driven codeless tool that enables multi-channel test automation across various domains. It facilitates continuous test automation and comprehensive business assurance by integrating business process-focused automation throughout the technology stack.

Key Attributes:
– Swift test automation development with minimal maintenance efforts.
– Unified Automation for Web, API, Mobile, and Desktop.
– In-Sprint Automation for DevOps and Agile Alignment.
– Manual testers can automate testing tasks without coding skills.
– Validation of business processes using a visual application model.
– Adaptation to fast-release changes with self-healing autonomic test automation.
– AI-powered generation of test cases and test data.

ACCELQ offers a free model and a subscription model with a complimentary trial period.

4. Applitools

Applitools is a comprehensive application of visual management and AI-powered visual UI testing and monitoring software. It offers a software testing platform driven by Visual AI, catering to professionals across engineering, test automation, manual QA, DevOps, and Digital Transformation teams. Applitools’ AI and machine learning algorithms analyze app screens in a manner similar to the human eye and brain, providing comprehensive testing capabilities.

Key Attributes:
– Integration with existing tests without the need for new scripts.
– Execution of tests using a single line of code.
– Visual test analytics and test management capabilities.
– Functionalities for cross-browser and cross-device testing.

Applitools offers three pricing models – Starter, Eyes, and Ultrafast Test Cloud, along with a free account option.

5. Testim

Testim is an AI-powered test automation platform designed for web applications. It enables rapid creation of UI and comprehensive end-to-end tests with AI stabilization. Testim is specifically tailored to facilitate Agile teams in managing and scaling their automation efforts effectively.

Key Attributes:
– Highly intuitive and user-friendly UX/UI design.
– Exceptional support team available for inquiries.
– Automated test generation without prior coding knowledge.

Testim offers free and premium versions, starting at $450 per year per test run (excluding setup fee).

6. Sauce Labs

Sauce Labs is an AI-driven tool that delivers comprehensive testing capabilities. It can be used throughout the software development process, ensuring continuous testing. The tool operates seamlessly across devices, browsers, and operating systems, offering automation for functional testing across various platforms. Sauce Labs provides developers with an in-depth understanding of the system or website’s operational behavior.

Key Attributes:
– Error reporting tool for monitoring and generating detailed error reports.
– Cross-browser solution for combined testing of various browsers and operating systems.
– Visual tool for automated visual and UI testing.
– Mobile tool for testing real and remote mobile devices.
– API tool for comprehensive testing and monitoring of APIs.

Sauce Labs offers a limited-feature free plan, along with monthly and annual subscription options.

7. Functionize

Functionize is a cloud-based automated testing technology that utilizes machine learning and artificial intelligence. It expedites test creation, diagnosis, and maintenance. Functionize stands out for its user-friendly approach, allowing test cases to be created effortlessly using plain English descriptions with the power of natural language processing (NLP). It can generate thousands of tests in minutes, covering a wide range of desktop and mobile browsers.

Key Attributes:
– Swift test case creation.
– AI-aided maintenance.
– Rapid debugging and effortless edits.
– Adaptive scalability for execution.

Functionize employs a quotation-based pricing model.


AI-driven tools for test automation offer enhanced efficiency, cost-effectiveness, and reliability in software testing. They have self-healing functionalities, ease of maintenance, and the ability to generate tests quickly. The AI-powered automation testing tools mentioned in this article are just a few of the popular options available in the market.

When choosing an AI-powered automation testing tool, it is important to define your goals for test automation and collaborate with versatile software testers or QA professionals who are adept at using these tools. With the right tools and expertise, you can achieve swift and error-free outcomes in your software testing endeavors.

If you need assistance or more information about AI automation tools, feel free to get in touch with us at

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Stop Talking to Yourself. Go beyond the RTCWEB Silo!

RTCWEB / WebRTC is designed to let two or more browser-enabled devices communicate P2P (peer-to-peer) with audio, video or data. But there’s a big catch. The browsers can’t communicate out of the box unless some undefined “external process” gathers information about each browser and hands the information to the other browser.

This mystical external process is known as “on the wire signaling”. Gathering information from a browser/peer needed to communicate isn’t incredibly difficult for a moderately talented programmer, nor is exchanging the required information. All that would be required is some kind of go-between web server and a socket or two. This solution is relatively simple and there are other companies setting themselves up to provide that kind of service offering.

But that kind of signaling will quickly becomes unwieldy to manage in the real world and misses many critical use cases and components in much larger deployments. The overriding presumption in such a model assumes both ends want to communicate and does not define how they want to communicate, let alone addressing very complex security issues.

So what does make up a robust and complete P2P communication solution?

A well thought out P2P solution should addresses these concerns:

  1. Initiation of communication between peers that are not actively expecting communication
  2. Exchanging the types of communication desired (audio/video/text/etc.)
  3. Allow peers the option to allow or disallow communication
  4. Allow peer to disengage communication at any time gracefully
  5. Changing the nature of the communication at any time (adding or removing media types like audio/video/text, media on hold, transferring sessions to other participants, etc.)
  6. Handle users’ identities so that users on independent systems can interoperate (and identify themselves when communicating)
  7. Handle users logged into multiple locations as the same user
  8. Find users to communicate with by their known identities (social, generic, 3rd party, etc)
  9. Validate the identity of the user you think you are connecting with
  10. Secure communication channels in a way that even servers involved in the “communication setup” are not able to decrypt information exchanged between peers
  11. Handle group conversations amongst peers without needing servers to relay the data
  12. Handle communication to applications outside to the browser (e.g. interoperate with mobile apps)

A well designed P2P platform should be designed to enable users on various websites to talk beyond each respective web silo. Users of one website can find and communicate with users on another website and even to users on mobile devices.

It should work with your existing identity model. Alice and Bob on your website are still known as Alice and Bob in the P2P network. You don’t need to administer and map a separate database of usernames and password that would be required with other legacy signaling protocols.

The network should allow users to locate other users by their social IDs, phone numbers, email addresses or by using your own custom defined identities – social or otherwise. It should be built with strong security in mind. Each user has their own private and public key, which when tied with an identity model yield strong proof of identity with completely private communications between peers.

A developer should be able to take the open source libraries and rapidly build and deploy powerful client applications with all of these features built-in and deploy without the headache of managing a communications network. No web developer I have ever met volunteered to be the one trying to figure out the complex ins-and-outs of everything that a good P2P design will resolve. So, I ask you, do you as a developer really want to be stuck in a little silo of communication, maintaining your own custom communication signalling protocol?

If you are looking to leverage WebRTC in a browser or if you just want to build a powerful communications feature into an app, you owe it to yourself to do the research. Before you get headlong into your project and find out the tech you chose was not up to the challenge, take a look around. Libraries like the one found in the Open Peer project could very well fit the bill.

Authored by Robin Raymond, edited by Erik Lagerway

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