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In the span of only a few months, AI has reshaped the landscape of almost every industry around the world in both positive and negative ways. Indeed, there is still a lot of room for improvement for this groundbreaking technology, but if businesses don’t embrace it, they’re sure to be left behind. In the QA industry, “AI testing” will become the norm in the next few years, bringing incredible advancements in the way we think and do software testing.
In this article, we’ll explore the concept of AI testing in-depth, see how AI can optimize testing, and share the pioneering AI testing tools that you can leverage to transform your QA workflows.
What is AI?
Before diving into the concept of AI testing, we must first have a common understanding of Artificial Intelligence (AI). AI has become a buzzword recently after the introduction of ChatGPT - a revolutionizing chatbot. So, what is AI?
AI can be defined as the simulation of human intelligence via machines that are programmed to perform tasks that require advanced cognitive abilities, such as data analysis, decision-making, learning, and adapting to new information. In a way, basic programming can only instruct computers to strictly perform a limited set of tasks, whereas AI systems have a certain degree of autonomy to them.
What is AI Testing?
AI testing is the process of evaluating the functionality, performance, and reliability of a system with the help of AI. It still involves the same core techniques used in traditional software testing, but these techniques have been drastically improved by AI technology.
When talking about traditional software testing, we usually think of a hybrid testing approach: partially automated and partially manual. Testers leverage automation testing tools to perform repetitive test cases so that they can focus more on tasks that require human reasoning such as decision-making and planning.
Once incorporated with AI, these tools will be able to assist testers in those advanced human reasoning tasks. It will bring the scalability of these tools to an even higher level, so testers can gradually move on to more strategic tasks. No longer just “testing tools”, they are AI-powered testing tools.
Read More: How To Write A Test Strategy?
AI testing can also be understood as testing for AI-based systems, or “testing for AI”. These AI systems incorporate various AI techniques, including machine learning, natural language processing (NLP), computer vision, deep learning, and expert systems, to process a tremendous amount of data to recognize patterns and make intelligent decisions.
Testing AI systems is similar to testing any other systems, and involves a six-step process from requirement analysis to test cycle closure. The major difference is that AI systems tend to also display extremely complex, diverse, and highly probabilistic behaviors. The number of scenarios to be tested is huge, and therefore
data-driven testing is usually the most common approach.
Benefits of Using AI for Testing
According to the State of Software Quality Report 2024, AI is most commonly applied for test case generation, both in manual testing (50% respondents agreed) and automation testing (37%). Test data generation is another domain where AI shines through, with 36%.
→ Download the State of Quality Report to get the latest insights on testing
Here are some more benefits of adopting AI in QA:
1. AI Enables Faster and Smarter Test Creation
AI can help QA testers generate test scripts both more quickly and intelligently. Usually these automated test scripts must be developed by a tester with engineering expertise using a test automation framework like Selenium; after that they have to be continuously maintained as changes are made to the source code. However, we can now provide a prompt in plain language that instructs the AI to create a test for a specific scenario, and it’ll write one in just seconds for you.
The trick is to know problem formulation, i.e., clearly defining the focus, scope, and boundaries of the test for the AI to generate new tests. Over time, as the AI learns more about the user behavior in your application under test (AUT), it can slowly adapt its test generation to fit with your specific business needs. It identifies your testing patterns by reviewing requirements, existing test cases, as well as logged test cases, to suggest the most relevant test cases for you.
2. AI Can Quickly Generate Test Data for Data-Driven Testing
In certain cases, to test comprehensively you would need a large volume of data, or a huge number of data combination inputs.
For example, testing the search functionality for thousands of different queries on existing products and locations of an e-commerce website. Instead of creating those data points from scratch or spending hours finding a suitable dataset on the internet, you can simply instruct the AI to generate a dataset for you. The high level of customization allows you to prepare mock test data for almost any scenarios you can think of.
Explore Katalon For Data-driven Testing
3. AI Makes Test Maintenance Effortless
Software and websites are updated all the time, especially in organizations that employ the Agile testing methodology. Test scripts written to test an element can quickly fail when the code is updated. For example, if we wrote a test to click on a button with the ID "login-button," changing this ID for any reason will cause the test to fail. Having to update a large number of test cases every time code changes occur (quite frequently) is a time-consuming process.
With the coding part now slightly offloaded to AI, testers are left with the daunting task of test maintenance, which can also be addressed easily with AI. AI can simply regenerate the test scripts each time a code change occurs. For example, if it can’t find an object with its current locator, the AI will attempt to use another locator and continue to run the test. This is known as the Self healing mechanism.
4. AI Enhances Visual Testing
Visual testing used to be quite a formidable task as human testers have to use their own eyes to spot visual differences between the UI-before-deployment and the UI-after-deployment. Visual testing tools can indeed identify visual changes better, but they can’t distinguish changes that affect the users and changes that do not. AI-powered visual testing tools, on the other hand, take that into consideration, and therefore can identify bugs more accurately.
Read More: What is UI Testing? A Comprehensive Guide
Using ChatGPT for Software Testing
ChatGPT is undoubtedly shaking up the AI world, and the tremendous advantages it can bring to software testers can’t be ignored. However, the trick to using ChatGPT is to master prompt engineering, and at a higher level, problem decomposition. With certain testing bottlenecks, we can now consider how a tool like ChatGPT can assist us, and then come up with a methodological solution that can be converted into a prompt. GPT won’t test for you, but it can be instructed to take over the more repetitive and mundane tasks, while QA teams move to the strategic planning areas.
AI will not render testers obsolete, and it is essential for QA engineers to familiarize themselves with AI and its potential to enhance software development. From a job market perspective, the rise of AI testing will actually intensify the demand for highly skilled and specialized QA experts that know AI prompt engineering to utilize vast volume input data to make decisions and reach conclusions faster.
AI-Powered Tools for AI Testing
The following software testing tools pioneer the AI testing trend and incorporate AI technologies into their systems to bring software testing to the next level. More than simply a tool to create and automate testing, they also perform intelligent tasks that in the past would have required a human tester.
1. Katalon Studio
Katalon Studio is a comprehensive quality management platform that supports test creation, management, execution, maintenance, and reporting for web, API, desktop, and even mobile applications across a wide variety of environments, all in one place, with minimal engineering and programming skill requirements. Katalon is an AI-powered testing platform with many AI features, including:
- StudioAssist: Leverages ChatGPT to autonomously generate test scripts from a plain language input and quickly explains test scripts for all stakeholders to understand.
- Katalon GPT-powered manual test case generator: Integrates with JIRA, reads the ticket’s description, extracts relevant information about software testing requirements, and outputs a set of comprehensive manual test cases tailored to the described test scenario.
- SmartWait: Automatically waits until all necessary elements are present on screen before continuing with the test.
- Self-healing: Automatically fixes broken element locators and uses those new locators in following test runs, reducing maintenance overhead.
- Visual testing: Indicates if a screenshot will be taken during test execution using Katalon Studio, then assesses the outcomes using Katalon TestOps. AI is used to identify significant alterations in UI layout and text content, minimizing false positive results and focusing on meaningful changes for human users.
- Test failure analysis: Automatically classifies failed test cases based on the underlying cause and suggests appropriate actions.
- Test flakiness: Understands the pattern of status changes from a test execution history and calculates the test's flakiness.
- Image locator for web and mobile app tests: Finds UI elements based on their visual appearance instead of relying on object attributes.
- Web service anomalies detection (TestOps): Identifies APIs with abnormal performance.
As one of the pioneers in the AI testing world, Katalon continues to add more exciting AI-powered features to their product portfolio, empowering QA teams around the world to test with unparalleled accuracy and efficiency.
Start testing with Katalon Studio now
2. TestCraft
TestCraft is an AI-powered test automation platform that revolutionizes regression and constant testing by leveraging the power of Selenium and offering comprehensive web application monitoring capabilities. They also leverage AI technology to ensure swift adaptation to changes in the application, thereby minimizing maintenance time and cost.
Key features:
- TestCraft has a user-friendly interface for constructing automated tests using drag and drop capabilities.
- Tests can be executed simultaneously on multiple browsers and work environments without coding skills.
- TestCraft's On-the-Fly mode generates a test model based on individual test scenarios, simplifying test step reuse and modifications.
- TestCraft uses a machine learning algorithm to identify web elements accurately, even with changes in the web application.
- TestCraft adapts to dynamic changes within web applications, minimizing test breakages and reducing maintenance time.
3. Applitools
Applitools is a software that manages visual applications and employs visual AI for AI-powered visual UI testing and monitoring. The incorporated AI and machine learning algorithms are fully adaptive, enabling it to scan and analyze app screens like the human eye and brain, but with the capabilities of a machine.
Key features:
- It effectively identifies visual bugs in apps, ensuring that no visual elements overlap, remain invisible, go off-page, or introduce unexpected features. Traditional functional tests fall short in achieving these objectives.
- Applitools Eyes accurately detects material differences and distinguishes between relevant and irrelevant ones.
- Automation suites sync with rapid application changes.
- Cross-browser testing is supported, but with limited AI features.
4. Testim Automate
Testim Automate is a test automation platform that uses machine learning to address two recurring challenges in software testing: slow test creation and extensive test maintenance. With Testim, individuals without coding skills can swiftly generate end-to-end tests using its recording functions. Engineers can also utilize code to expand on these capabilities, combining the best of both approaches.
Testim improves test maintenance with its innovative smart locators method. This approach analyzes every element used in testing and assigns weights to hundreds of attributes for each element. Therefore, even if one attribute, like an ID, changes, Testim can still locate the element, preventing test failures. Best of all, this process is straightforward and avoids the need for complex and error-prone queries.
How to Test for AI Systems
Testing for AI systems comes with unique challenges, and requires specialized techniques:
- The results of these AI-based systems are non-deterministic, i.e., they generate different results for the same input.
- There is usually human bias in the training and testing data, which needs to be identified and eliminated during AI model testing.
- AI performs best when given advanced input models. If the inputs are not of high quality, the issues become more complicated, requiring a significant amount of time and effort to address.
- AI is an intricate system, and even small defects are magnified significantly. This problem becomes more challenging to resolve as well.
There are several best practices when performing testing on these AI systems:
- Clearly define the objectives, scope, and success criteria of your AI testing efforts. Determine the specific aspects of the AI system to be tested, such as accuracy, performance, robustness, and fairness.
- Ensure high-quality training data is used to train the AI models. The training data should be representative, diverse, and cover various scenarios to promote accurate and unbiased learning.
- Establish baseline metrics or results to compare the AI system's performance against known benchmarks.
- Leverage data-driven testing to achieve higher efficiency. AI-powered software testing tools listed above, such as Katalon, Testim, and Applitools, all support data-driven testing in many popular data formats to help you test AI-based systems better.
AI Testing FAQs
1. How is AI testing different from traditional software testing?
AI testing differs from traditional software testing in that it leverages AI-powered testing tools to improve testing efficiency and effectiveness. Traditional software testing primarily relies on manual efforts, while AI testing incorporates automated test case generation, execution, and analysis using AI algorithms. AI testing also involves testing AI models themselves, ensuring their reliability, accuracy, and mitigation of biases.
2. What challenges are involved in AI testing?
AI testing introduces unique challenges, including the need for understanding and validating AI model behavior, addressing model biases and limitations, maintaining and updating AI models and datasets, and integrating AI-powered testing tools into existing testing processes.
3. How can AI support continuous testing?
AI-powered test automation frameworks can help to create tests continuously and efficiently, as well as detect changes in the AUT and trigger appropriate tests. AI algorithms can also analyze test results and provide insights on failures, trends, and areas that require further testing, enabling teams to continuously improve their testing processes.
