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In just a few months, AI has revolutionized industries worldwide, including QA. While still evolving, AI testing is set to become the norm, transforming software testing processes.
This article dives into AI testing, its impact on optimization, and the leading AI tools to enhance 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.
Testing For AI System
The "AI testing" term can also be understood as testing for AI-based systems, or “testing for AI”. To process a tremendous amount of data to recognize patterns and make intelligent decisions, these AI systems incorporate many AI techniques, including:
- Machine learning
- Natural language processing (NLP)
- Computer vision
- Deep learning
- Expert systems
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. Faster and Smarter Test Creation
AI accelerates test script creation by allowing testers to use plain language prompts instead of manual coding. It analyzes application behavior, existing tests, and patterns to adapt and suggest relevant tests aligned with business needs. The key is clear problem formulation to guide AI in generating effective tests.
2. Automated Test Data Generation
AI simplifies data-driven testing by generating large, customized datasets for comprehensive testing. For example, it can instantly create thousands of input combinations, such as search queries for e-commerce websites, saving time compared to manual data preparation.
3. Effortless Test Maintenance
AI reduces the burden of maintaining test scripts in dynamic environments, like Agile projects. It employs self-healing mechanisms to adjust scripts automatically when changes occur, such as updating locators for UI elements, minimizing script failures and manual corrections.
4. Enhanced Visual Testing
AI-powered visual testing tools go beyond traditional tools by identifying meaningful visual changes that impact user experience. These tools can differentiate between irrelevant UI shifts and critical bugs, delivering more accurate results with less manual effort.
Read More: What is UI Testing? A Comprehensive Guide
Using ChatGPT for Software Testing
ChatGPT is revolutionizing software testing by streamlining repetitive tasks through effective prompt engineering and problem decomposition. While it won’t replace testers, it enhances their efficiency, allowing QA teams to focus on strategic planning.
AI testing won't make QA roles obsolete; instead, it increases the demand for skilled QA engineers proficient in AI tools and prompt engineering, essential for leveraging vast input data and accelerating decision-making.
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.
For AI testing specifically, here are the key features you can have:
- 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, 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.
