What is AI-based Software Testing & Quality Assurance | DeviQA
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What is AI-based Software Testing & Quality Assurance

What is AI-based Software Testing & Quality Assurance
February 26, 2024


Software testing is an industry that is continuously evolving. The constant need for faster, more efficient, and comprehensive software testing means it's no surprise that the QA industry has undergone numerous changes over the past few decades.

Currently, we live in interesting times where manual testing still holds value, while automation testing and continuous testing are also prominent. Moreover, efforts are being made to integrate artificial intelligence into software testing to achieve even better results. All in all, the future of the QA industry is closely linked with AI, which has given rise to the phenomenon of AI-based software testing. Let's explore what this entails and its prospects in detail.

AI in Software Testing and Quality Assurance

To gain a clear understanding of the subject, we suggest starting from the basics.

Artificial intelligence can be defined as an area of computer science focused on enabling systems to perform tasks that normally require human intelligence.

To imitate human behavior, AI systems process vast amounts of data for training, analyze it to reveal dependencies and patterns that will be used for making predictions about future states. It is important that such systems constantly fine-tune their algorithms to provide as accurate results as possible.

The main components of artificial intelligence include Machine Learning, neural networks, deep learning, and Natural Language Processing (NLP).

As for software testing, it is an essential part of software development that involves exploring and testing software in the most diverse ways to ensure its high quality and compliance with project requirements.

Consequently, AI in software testing leverages AI algorithms in automation testing tools to optimize the QA process, making it as autonomous and efficient as possible.

What is AI-based Software Testing?

AI-based software testing is a buzzword in the QA community, so let's figure out what it is.

AI-based software testing is a testing technique that involves the use of AI-powered solutions to effectively test a software product. The main purpose of AI-based testing is to make the QA process smarter, quicker, and more effective. The capabilities of AI, such as data processing, self-learning, decision-making, reasoning, predicting, and others, when properly applied, can be extremely beneficial for software testing and open new horizons by turning software testing into a completely autonomous process.

Currently, AI-based software testing is on its way to maturity. Certain attempts have already been made to embed basic AI algorithms into some testing tools that streamline specific facets of software testing. However, the main breakthroughs in this realm are still in the future.

How is AI improving the Software Testing and Quality Assurance process?

As mentioned before, practices of AI-based testing are currently in their early stages and require further development and improvement. However, certain advances have already been made, and many projects are indeed promising. So, let's explore how AI can be beneficial for software testing.

Accurate testing results

Considering all the capabilities of artificial intelligence, we can confidently state that software testing executed by AI is more accurate than that executed by a human tester, who is always susceptible to stress, tiredness, overloads, boredom, and other factors that can lead to mistakes and negligence. AI-powered tools, in these terms, are more efficient as they can perform testing whenever needed with high accuracy.

Self-healed Selenium tests

Test flakiness is one of the major challenges of automation testing, but AI can help to overcome it. For example, quite a few AI-driven tools for Selenium test self-healing have been introduced to the market.

AI algorithms create appropriate locator strategies to save QA teams time, eliminate maintenance hassles, and ensure test stability. If one of the locators is broken or missing, the self-healing technology finds the most appropriate alternative locator and heals the test on the fly during a test run, informing the tester about how the test has been modified.

Complete traceability

Integrated AI-driven analytics collect test outcomes and associate your tests with project requirements and code coverage to provide complete test traceability.

Efficient visual validation

The ability of AI to recognize images has been actively utilized in automated visual validation tools that verify whether UI elements have the correct color, position, size, etc., and detect bugs that are usually missed during manual testing.

These tools also tackle the challenge of ongoing changes in UI, along with the constantly growing number of platforms and devices with different screen sizes and configurations.

All in all, AI has made UI testing faster and more reliable.

Selective testing

AI-powered tools can optimize software testing by executing only those tests that are required to check code changes in a new build. In this way, there is no need to execute thousands of time-intensive tests, but only the necessary ones, to provide fast feedback.

Better test coverage

AI-powered automation testing tools can execute many more tests of the most diverse types within a short timeframe. In this way, QA teams can achieve higher test coverage, and product quality significantly improves as well.

Test data generation

QA experts understand that test data plays a crucial role in the effective software testing process. Therefore, dedicating time to data creation/collection is important. Thanks to AI, this process has become much easier, as test data can now be generated automatically based on the analysis of production data. Consequently, AI-generated test data closely resembles real data, which makes testing more accurate.

Reduced time to market

As AI significantly accelerates test execution, test maintenance, test data generation, and many other aspects of the QA process, it contributes substantially to reducing time to market, which is of high value for businesses.

The best AI tools for Software Testing

Considering the power of AI, numerous attempts have been made to utilize it in software testing by implementing it in various automation testing tools. Nowadays, there is a plethora of AI-driven testing tools on the market. Some of them have proven to be especially efficient and became popular immediately. It is worth noting that most AI-based testing tools are commercial, and even if they offer a free version, it typically provides a limited number of options compared to the premium one. Below, you can find a brief overview of the most commonly used AI testing tools.


Applitools offers an AI-driven visual UI testing tool designed to automatically assess the appearance of applications and websites. It integrates seamlessly with existing tests, eliminating the need for creating new tests or learning a new test automation language. With Applitools, validating the entire screen of an application is possible with just a single code snippet, allowing for quick checks of visual consistency across all browsers and screens.


Testim provides an automated functional testing platform that leverages AI and ML to enhance test design, execution, and maintenance. It distinguishes itself not merely as a tool but as a comprehensive platform, including a test execution environment. Testim supports various browsers and platforms, offering test creation through a recorder, code, or a combination of both. It uses historical data to improve locator accuracy for UI elements and offers visual validations for elements like pixels, fonts, and text.


Test.AI functions as an Appium element finding plugin, utilizing AI-based search strategies for identifying UI elements. Its primary aim is to facilitate code-free test automation, enabling autonomous element selection and test execution. Test.AI employs specialized bots to assess application functionality during testing and conducts regression testing, including screenshot captures and performance metric analysis.


Mabl specializes in regression testing, utilizing AI trained on user interactions and intents to replicate user input accurately. It also performs checks for broken links, JavaScript errors, visual changes, and variations in page loading/test execution times. Mabl's test accuracy improves with each run, as it learns to distinguish more effectively between dynamic and static regions for visual testing.

Eggplant AI

Eggplant AI is an automation testing tool that uses artificial intelligence and machine learning to auto-generate tests, optimize test execution, identify bugs, and achieve comprehensive test coverage. It focuses on delivering a superior end-user experience by testing from the user's perspective, covering app usability, functionality, and performance across a wide range of browsers, operating systems, and devices. Eggplant AI also features regression packs, bug hunting, and coverage analysis.

The Future of AI-based Software Testing & Quality Assurance (continued)

While AI-based software testing is still in its early stages, DeviQA is at the forefront of this exciting new frontier. We offer a comprehensive suite of AI-powered testing services designed to help you elevate your software quality and accelerate your time to market.

Our team of experienced AI specialists has the expertise to leverage the latest AI advancements and tailor testing solutions to meet your specific needs. We can help you:

Automate repetitive testing tasks to free up your QA team to focus on more strategic initiatives.
Improve test accuracy and coverage by leveraging AI's ability to learn and adapt.
Reduce time to market by streamlining the testing process.
Enhance user experience by ensuring your software is bug-free and easy to use.

If you're looking to take your software testing to the next level, contact DeviQA today to learn more about our AI-powered testing services.

Visit our AI Testing Services page to learn more about how we can help you leverage the power of AI to improve your software quality.


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