AI in QA and testing: benefits, challenges, main technologies to use

AI in quality assurance (QA) and testing is about using AI to improve software development. Artificial intelligence (AI) systems utilize techniques like machine learning (ML) to interpret data and come up with logical actions.

So, what does a tester do?

Testing experts are tasked with analyzing a project, creating test cases, and thoroughly checking everything to ensure it follows the standard best practices and techniques. With AI, the amount of testing a tester can complete is significantly enhanced and is the main topic of discussion.

For those looking for tools to use AI in testing, artificial intelligence can be leveraged to create tests, improve execution, and help with future maintenance, ensuring high code quality from development to deployment.

It can also be used for predictive analysis to prevent defects in high-risk areas, enhance continuous integration and deployment pipelines, security testing, user behavior analysis, and more.Let’s dive and explore what to expect when integrating AI into quality assurance.

Artificial intelligence in software testing overview

AI in software testing has changed the approach to testing. With AI, software can be made more reliable, effective, and efficient. AI-powered tools make it possible to automate repetitive and mundane tasks, giving developers extra time to focus on more complex testing scenarios.

These tools can also be used to identify and predict defects in the software, ensuring that the testing process is more thorough and accurate.

The use of AI in QA and testing has become increasingly important because companies are always seeking to reduce the time and effort required during the testing and deployment phases. Automating repetitive testing tasks helps businesses improve the overall quality of their software and achieve faster release cycles.

So, how does AI affect the quality and speed of QA work? Software Testing Manager at EPAM, Nataliya Bogushevskaya responds,

“Tricky question :) From the experience I can say that without basic understanding on how AI, especially Generative AI, works, using AI tools for QA work rather leads to wasting of time. However, if you apply proper patterns, techniques and frameworks; if you understand how to craft a prompt and how to tune this prompt, speed can be increased from 20 to 40%.”

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Key benefits of using AI in QA and testing

We’ve already mentioned how integrating AI into the testing processes can enhance effectiveness and efficiency. Let’s now discuss in detail the key benefits that come from using AI in quality assurance and testing.

Complements manual testing

Manual testers are diligent and highly skilled. Nonetheless, manual testing has its drawbacks because there’s a limit to what human testers can handle in a specific timeframe, moreso in cases where they’re dealing with complex applications.

In such circumstances, AI steps in to effortlessly handle time-consuming and repetitive tasks and large volumes of data. When combined with human expertise, you end up with a comprehensive testing approach.

It can also support creativity by giving skilled testers the opportunity to do what they’re best at — uncovering the unexpected through critical thinking.

After all, manual testers can engage in more exploratory testing when they no longer worry about monotonous tasks. With that reprieve, a software functional testing engineer can, for instance, dig deep into the software’s functionalities and identify overlooked edge cases.

Improves quality assurance and test coverage

If you’re unsure of whether your tests cover all possible scenarios or not, then incorporating AI into your software testing could be the answer.

AI is capable of analyzing vast amounts of data and identifying any elusive gaps that could have gone unnoticed during manual testing and improve the overall quality of your software.

For instance, if you’re developing a complex mobile application that has a number of functionalities, features, and potential scenarios. Manual testing could cover all the obvious cases, but there might be hidden nooks and crannies where bugs could be lurking undetected. Now, it is at this point that AI in software testing comes in to save the day.

A subset of AI, generative AI, remarkably boosts testing coverage areas. Based on your software’s specifications, generative AI can autonomously generate test cases. This makes it possible to cover a wide range of scenarios without the need to manually create each test.

AI is about more than just the quantity covered; it also enhances test quality. AI uses comprehensive tests to identify repetitive patterns and problems that could indicate underlying problems.

This lets development teams address potential issues proactively and improve overall software quality even before it reaches end users. With Anywhere Club’s test automation foundations course, you can unlock so much more in testing, especially with AI by your side.

Speeds up timelines

Among the key benefits combining AI and software testing is swift yet detailed scanning of log files, code in seconds and discovering defects much faster than manual testers who require more time to manually go through thousands of lines of code.

Enhances cost efficiency

AI in testing could carry out tedious and repetitive work on a regular basis without costing extra money.

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Challenges of AI in QA testing

Here are some limitations of artificial intelligence in quality assurance testing:

• Lacks creativity: AI in quality assurance and software testing experiences a big challenge when generating test cases that focus on unexpected scenarios or edge cases.
• Requires “perfect conditions”: The use cases in project documentation can sometimes be vague or unrealistic, however, AI can only produce work whose quality is exactly as bad or as good as the quality of quantifiable data from the input or context.
• Has limited understanding of the software: AI may not have a thorough understanding of the software being tested, this could result in missing important defects or scenarios.
• Depends on training data: The quality of AI-generated test cases fully depends on the quality of the data used to train the algorithms, and sometimes, the data could be biased or limited.
• Requires skilled professionals to operate: Integrating testing strategy with AI-powered CI/CD pipelines for example, can be quite difficult to set up, maintain, and troubleshoot because it requires advanced knowledge and technical skills.

What are the biggest challenges of using AI for testing that you have faced? How did you manage to overcome them?

“As for me, the biggest challenge is to invest the time in learning and practicing. You can watch a lot of training about LLMs, AI and prompting. But without daily practice they will be useless. Using AI is like onboarding a junior to the project. Smart junior that knows theory but doesn’t have a clue about project specific. You have to spend time and adopt this junior to your requirements and expectations.” answers Bogushevskaya.

Leading technologies for AI software testing

Following the automation testing career path means you will encounter and use advanced tools and techniques to automate the testing process, improve accuracy, and reduce the time needed for software validation. Leading technologies in this sphere include:

1.   AI-powered test automation tools

• Selenium: Although this tool isn’t inherently AI-based, it is widely used to automate web applications for testing purposes.
• Testim.io: This tool makes use of machine learning to create, execute, and maintain tests while adapting to changes in the UI and making tests more resilient.
• Mabl: It combines AI with traditional test automation to improve test coverage and maintenance by automatically identifying and addressing changes in the application.

2.   Natural language processing (NLP)

• Applitools: Uses visual AI to automatically verify that the UI of an application appears correctly across various devices and browsers.
• ReTest: Implements AI for UI testing, leveraging NLP to understand user interface changes and ensure that visual elements behave as expected.

3.   Model-based testing

• Conformiq: Generates test cases from models of the system under test, using AI to optimize test paths and improve coverage.
• Test.ai: Uses AI to explore applications and automatically generate test cases based on observed behaviors and patterns.

4.   Predictive analytics

• SeaLights: Analyzes test execution data to predict which tests are likely to fail and prioritizes them accordingly, reducing the overall testing effort.
• Appvance IQ: Uses machine learning to identify patterns in test failures and predict potential issues, improving test efficiency and effectiveness.

5.   Self-healing tests

• Functionize: Employs AI to detect and repair broken tests automatically, reducing the need for manual intervention and ensuring continuous testing without interruptions.
• Eggplant AI: Uses AI to generate test cases, execute them, and learn from the results, continuously improving the testing process.

6.   Smart test execution

• TestSigma: Utilizes AI to identify the most critical tests to execute based on code changes and historical test data, optimizing the test suite for maximum coverage and efficiency.
• Launchable: Uses machine learning to predict the risk of changes and dynamically selects the most relevant tests to run, improving test accuracy and speed.

7.   Cognitive computing

• IBM Watson AI: Applied in various testing scenarios, Watson's cognitive computing capabilities help in understanding natural language requirements and generating relevant test cases.

8.   Robotic process automation (RPA)

• UiPath: A QA automation engineer can leverage RPA and AI to automate repetitive testing tasks, integrating seamlessly with existing test automation frameworks to improve efficiency.

To help those taking the journey to learn how to become an automation tester, Bogushevskaya has the following to say about the tools she recommends: “You can start with ChatGPT 3.5 and Gemini. They are free and can be used for many typical QA tasks. They might not be as ‘clever’ as paid versions, but they solve general tasks pretty well. If we talk about particular tasks, it depends. For example, Claude 3 might be better for code generation compared with GPT for JavaScript. But not for .NET.”

Main methods of using AI for software testing

Here are some of the main methods of using AI for software testing:

Test case generation

In Model-Based Testing (MBT), AI algorithms are used to generate test cases by analyzing the models of the software’s expected behavior. For automated test script generation, AI tools convert manual test cases into automated scripts by learning from user interactions and testing patterns.

Test execution

AI in testing software is used to automate regression testing by identifying the test cases to rerun based on code changes, thus optimizing the regression test suite. AI also does intelligent test execution by prioritizing and executing test cases based on risk, impact, and historical data.

Test data generation

AI does synthetic data generation by creating diverse and realistic test data to stimulate various scenarios, including edge cases. Through data augmentation, AI enhances existing datasets to improve variability and coverage in testing.

Defect prediction and classification

AI uses bug prediction models to predict areas of the code that likely contain defects, giving testers the chance to focus on more high-risk areas. In software testing, AI prioritizes and categorizes defects, aiding in efficient bug triaging and resolution.

Test optimization

AI analyzes test suites to identify obsolete or redundant tests, helping to optimize and streamline testing efforts. It also predicts the test cases that are most likely to fail based on historical data and code changes, focusing efforts on critical tests.

We asked Bogushevskaya to tell us about the main advantages of the AI-supported QA course and its role in helping testers create a QA automation testing roadmap. Her response was, “This course combines theory and practical implementation; it’s aligned with typical QA tasks in STLC.”

Further, who is the AI-supported QA course designed for? Is it suitable only for beginners, or also for intermediate and advanced specialists? According to Bogushevskaya, “The course is designed for any level. Beginners can simply reuse ideas and prompts listed in the course; while advanced specialists can solve complex tasks, inspired by examples from the course.”

Lastly, what advantages does the course have over other QA training programs? “Well, after completing the course you will be able to create prompts for solving quizzes from other QA trainings.”

Future of AI in quality assurance

In the near future, deep learning-based AI algorithms are expected to evolve into self-acting entities capable of creating appropriate test cases all on their own, autonomously detecting issues, and adjusting dynamically should there be any software changes to minimize manual intervention during maintenance.

AI algorithms and predictive tests are about to be integrated into the essentials of AI quality assurance. Quantum computing is set to advance AI testing through higher processing power that will make it possible to simulate complicated situations.

Challenges that were once considered unattainable through ordinary computing can be tackled by advanced technologies while skills for QA tester jobs are leveraged to better understand often overlooked aspects of the testing process.

Bogushevskaya also shared her thoughts on whether AI testers will replace humans.

In her opinion, “No! Can AI make a tester more productive? For sure! Can AI be taught to solve routine and boring tasks — yes! Can AI replace testers? Even human developers struggle with that.”

How to learn AI QA testing with courses from Anywhere Club

You can learn AI QA testing effectively with courses from Anywhere Club. You’ll gain practical skills through hands-on projects, expert-led tutorials, and comprehensive study materials. The courses have flexible online access that will allow you to learn at your own pace, ensuring you stay ahead in the fast-evolving field of AI for QA testing.

So, do testers need special training to use AI in their work?

According to Bogushevskaya, “Yes. Let me share several examples of when AI can be used, but testers didn’t realize that.

Project 1. Requirements often were provided as Figma design only. Manual testers had manually put all the attributes from the mockup and retrieved colors of elements in a textual format. I showed them how Gemini (or Product Monkey) can be used for the same. Test case creation now takes several minutes instead of several hours.

Project 2. Application development migrated to GCP (Google Cloud Platform), however, manual testers didn’t have enough skills in gcloud scripting. Each time they needed to check something in logs/VMs/etc, they were creating a new ticket on DevOps team and were waiting for results.  

With GPT they started to write scripts without DevOps team (maybe not perfect scripts but working).”

Suffice it to say that the career of a software tester is rewarding, if you can stay at the bleeding edge and work with the new technologies streaming into the market. Start learning today at Anywhere Club and upskill to offer more value.

Good luck!