How to Use AI in Software Testing: Types, Use Cases and Process

Software testing is essential for delivering high-quality software applications. But it’s not without challenges, such as tight release deadlines, repetitive tasks, and the risk of human errors.

This is where AI in software testing can help teams automate tedious processes, making testing faster and more efficient. It helps detect defects earlier, simulate user behavior, and even predict potential failures. Be it UI or visual testing, AI takes testing beyond traditional methods, allowing testers to focus on critical tasks while reducing time and costs.

In this blog, we look at how to leverage AI in software testing.

What Is AI in Software Testing?

While traditional methods of software testing heavily rely on predefined scripts and manual effort, AI and software testing include intelligent automation for enhancing different aspects of a Software Testing Life Cycle (STLC). It also leverages machine learning models for analyzing large volumes of test data, identifying patterns indicating potential defects, and generating optimized test cases.

It’s imperative to incorporate AI in software testing since it has the power to expedite the entire testing process. Adapting to ever-evolving software needs and conducting in-depth data analysis, AI in software testing contributes to empowering a testing ecosystem that’s more efficient, adaptive, and comprehensive. Ultimately, it improves the quality of software releases.

Why Use AI In Software Testing?

When you integrate AI into software testing, it opens up new avenues for addressing issues that come with traditional testing methods.

Here are some of the benefits of AI in software testing:

• Improve Test Accuracy: AI-powered software testing tools leverage the power of predictive analytics to recognize any potential bottlenecks, which minimizes human error.

AI is also capable of analyzing historical test data for predicting software areas with a high possibility of failing, which facilitates targeted testing. It also reduces the risk of releasing a product with bugs that went undetected.

• Faster Test Execution: AI uses machine learning algorithms for automating any repetitive tasks and optimizing test processes, which, in turn, accelerates the execution of tests.

You can quickly execute tests across different environments with the help of AI tools. It also facilitates continuous testing so that the development teams can receive quicker feedback on the quality of the software.

• Enhance Test Coverage: AI in software testing reduces friction in autonomous test creation and makes both test maintenance and creation less time-consuming. It leads to full test coverage results as tests can run more often and the tool can identify bugs earlier as compared to a full manual approach or a tool without AI.

The best part is that the QA team can easily allocate the majority of that time towards building even more automated tests, defining edge cases, and testing brand-new features instead of going through the same test cases again and again manually.

• Test Flakiness: AI in software testing establishes dynamic test stabilization, puts enhanced locator strategies in place, and leverages the power of real-time diagnostics to address this issue. For instance, AI algorithms conduct continuous analysis of patterns in test execution for identifying and resolving any flaky behaviors.
• Better Test Maintenance And Stability: Traditional testing tends to have third-party and other dependencies, which can sometimes lead to the breaking of tests. Even maintaining existing tests can turn out to be a cumbersome task every single day.

AI streamlines maintenance by adapting to changes in software by detecting modifications in user interface elements and accordingly updating the corresponding test scripts.

Note

Address flaky test issues with AI-powered test intelligence. Try LambdaTest Today!

How to Use AI In Software Testing?

In this section, we are going to take a look at how you can use AI in testing to make the most out of your STLC.

1. Define Objectives: AI isn’t fully autonomous yet, but it can take on heavy lifting, reducing manual work for testers. Start by identifying what you aim to achieve with AI.

For example, if your team struggles with limited resources, you might want AI to handle scripting tasks. Clear goals like this help you decide on the right tools and technologies, such as predictive analytics or natural language processing.

2. Leverage AI Technologies: Once your objectives are clear, pick the AI tools and technologies that align with your needs. For instance, if you need AI to write test cases due to resource constraints, NLP is a great option. It lets you write test cases in plain English, which AI models can interpret and act on.
3. Train the AI Algorithms: Now that you’ve chosen your technology, train the algorithms using your organization’s data. This step is critical to ensure the AI understands your requirements and delivers results specific to your project. It’s a good idea to involve an AI expert for accurate training.
4. Measure Efficiency and Accuracy: Training alone doesn’t guarantee success. You need to test the AI algorithm to ensure it’s accurate and efficient. To test an AI algorithm, you can use techniques like model interpretability testing, bias and fairness testing, black-box testing, and white-box testing.
5. Integrate With Test Infrastructure: Once the AI model passes these tests, integrate it into your test setup. It ensures smoother workflows and helps your team harness the power of AI effectively.

Types of AI Software Testing

AI is applicable in different types of testing, which helps contribute to increased adaptability, accuracy, and speed in all processes.

On this note, let’s take a look at the four testing types in which AI promises the highest potential to boost precision and efficiency.

• Functional Testing: AI can enhance functional testing by generating realistic test data that closely mimic real user inputs, thus improving the quality of test cases. It automates data-driven testing, which makes up the bulk of functional testing, allowing testers to focus on more strategic tasks. AI also analyzes user behavior and interaction with the software application, helping to prioritize test cases based on critical user flows.
• Non-Functional Testing: AI significantly boosts non-functional testing, particularly in performance testing, by analyzing large volumes of historical data to uncover potential bottlenecks. AI also optimizes resource distribution by balancing load and ensuring efficient resource usage during performance tests.
• Unit Testing: AI tools help in the automatic generation of unit test cases by analyzing the code structure and behavior, ensuring comprehensive test coverage. They identify areas often missed in manual testing and predict potential bugs by analyzing code patterns and highlighting areas prone to errors.
• Visual Testing: AI complements visual testing by detecting even the smallest visual discrepancies that might be overlooked by human testers. It enhances the effectiveness of identifying visual anomalies in the UI, especially in areas where minor details impact user experience.

AI tools can compare screenshots and detect UI changes, ensuring that visual consistency is maintained across different versions of the software. For example, cloud testing platforms like LambdaTest offer a SmartUI platform to perform smart visual UI testing. The SmartUI comes with AI-powered capabilities that let developers and testers enhance their visual comparison testing.

Use Cases of AI in Software Testing

AI-based test automation uses ML techniques along with advanced algorithms to enhance the reliability, accuracy, and efficiency of various testing processes.

Let’s take a look at some primary methods for AI test automation.

• Self-Healing Test Automation: Automation scripts can often break because of UI changes, object properties, and other WebElements. AI-based self-healing tools carry out the monitoring of these changes as well as adapting to them in real-time.

Whenever there’s an issue with a script, the AI system leads to automatic identification and resolution of the problem which ensures uninterrupted testing. Therefore, it eliminates the requirement for manually correcting the script, which leads to the enhancement of the test frameworks, resilience, and a reduction in the maintenance overhead.

• Intelligent Analysis and Defect Prediction: AI harnesses Natural Language Processing and machine learning for analyzing application logs and historical defect data which identifies patterns, signaling potential problems.

Such a method enables early prioritization and detection of false positives by enabling teams to cater to any vulnerabilities and bottlenecks before they have a chance to escalate. Such an approach also decreases development risks and accelerates the software testing life cycle by predicting areas prone to defects and supporting reliable software and a faster time to Market.

• Regression Test Automation: Regression testing can turn out to be a resource-intensive and critical task. Moreover, verifying the functionality so that any new changes to the code won’t have any impact on existing functionality can be challenging.

An AI-powered regression suite intelligently analyzes changes in code and chooses test cases that are the most relevant to optimize this process. Such a targeted approach ends up producing the effort and time needed for regression cycles, which maintains high-quality software while ensuring faster feedback.

AI Software Testing Tools

Let’s move on to a list of the top AI testing tools proven to be worthwhile. Not only do they assist in risk management by detecting future challenges, but they also reduce the time-to-market and ensure higher software quality with the help of real-time monitoring.

KaneAI

KaneAI by LambdaTest is a GenAI native QA Agent-as-a-Service platform designed for high-speed QA teams seeking to automate multiple aspects of testing, including test case authoring, debugging, and management.

This smart test assistant powered by AI allows teams to create complex test cases that are highly evolved with the help of natural languages, which fastens the pace of test automation and makes it more intuitive.

Features:

• Test Creation: Enables the development and refinement of tests through natural language instructions, making automation approachable for users of all skill levels.
• Intelligent Test Planner: Generates and executes test steps based on high-level objectives, streamlining the test creation process.
• Multi-Language Code Export: Transforms tests into various major programming languages and frameworks, providing flexibility in automation.
• 2-Way Test Editing: Syncs natural language edits with code, allowing modifications from either interface.
• Integrated Collaboration: Supports tagging KaneAI in tools like Slack, Jira, or GitHub to initiate automation, enhancing teamwork and efficiency.

ACCELQ

It is a popular cloud-based platform to automate and manage AI-powered tests. It provides many enterprise applications with codeless test automation and automates the entire enterprise stack through desktop, API, mobile, and web. It also ensures reliable test execution by providing long-term automation with the help of AI.

Testim.io

Testim.io simplifies and enhances testing by enabling testers to create automated tests using minimal coding. This AI platform uses ML for adapting and stabilizing testing and reducing maintenance overhead. That’s a consequence of frequent updates.

TestComplete

TestComplete offers dynamic testing features that are AI-driven, including an important one known as checkpoints responsible for testing tables, images, and application settings. It enables testers to create, maintain, and perform various functional tests across web, mobile, and desktop applications.

Katalon Studio

Katalon Studio has turned out to be one of the most comprehensive AI testing tools that offers support for desktop, mobile, API, and web applications. It improves the overall process of test automation by incorporating AI capabilities such as automatic script generation, smart object detection, and auto-healing for test flakiness.

AI in Software Testing With KaneAI

It’s ideal to adopt a tool with tester-focused features and AI capabilities that automate most tasks using machine learning. This is where test agents like KaneAI by LambdaTest can help.

Unlike traditional low-code/no-code solutions, KaneAI tackles scalability issues effectively. It handles intricate workflows, supports all major programming languages and frameworks, and delivers seamless performance for even the most advanced AI testing scenarios.
In this example, we’ll verify the add-to-cart functionality by adding a product to the cart on the LambdaTest eCommerce Playground.

1. From the LambdaTest dashboard, select the KaneAI option.
2. Click on the Create a Web Test button to open a browser with a side panel for writing test cases.



3. In the side panel, you can either manually interact with the browser (actions will be recorded) or write test steps in the text area.

For demonstration, let’s write the steps in textarea and press Enter. The test step is recorded, and the website opens in the browser.

We will add the following test steps:

1. Go to https://ecommerce-playground.lambdatest.io/.
2. Type the search query canon in the Search For Products search box.
3. Click on Canon EOS 5D.



4. Click Finish Test at the top-right corner to complete the session. You can now save the test case. Fields like Test Name and Description are auto-populated by AI, saving time. Tests can be organized in folders and reused later as needed.

Shortcomings of AI in Software Testing

While AI has already proven to be a powerful technique in software testing by enhancing efficiency in automating repetitive testing tasks, it does have a set of shortcomings.

• Testing for Complex Scenarios: AI struggles with complex scenarios involving creativity and domain knowledge, such as multi-module interactions, real-world behaviors, or compliance testing. Human expertise is essential to ensure these scenarios are thoroughly tested and aligned with user expectations.
• UX Testing: While AI can flag usability issues, it cannot evaluate subjective user experiences or emotional responses. Human testers provide insights on design, usability, and workflows to ensure applications meet user needs and uphold brand standards.
• Documentation Review: AI cannot fully grasp business logic or domain-specific nuances, making documentation review reliant on human judgment. Business analysts and QA testers collaborate with stakeholders to clarify priorities and solidify the foundation for effective testing.
• Test Reporting and Analysis: AI generates detailed test reports but lacks the context needed for actionable insights. Human testers interpret results, prioritize fixes, and ensure critical issues are addressed effectively.

Future of AI in Software Testing

AI has become a trend that’s quickly gaining pace this decade. For obvious reasons, it’s expected to continue for the long haul. Chances are, frameworks and tools will leverage the power of AI for further enhancements and targeting specific issues related to testing, especially in auto-correcting of element selectors, UI element location, visual testing, and functional testing of both mobile and web applications.

AI automation in testing is expected to take over various tasks at every testing level, most of which require decisions a human is able to make in a split second. Even though higher-order tasks might still need some level of human intervention, AI might take over as machines become more and more trained on actions pertaining to these activities, requiring deeper context.

Quantum computing and software testing have the potential to take AI testing to great heights thanks to higher processing power. Since AI makes use of ML, predicting any possible weaknesses beforehand will enable action prior to the possible occurrence of a negative outcome.

With the rise of self-healing systems and intelligent automation in testing, AI algorithms based on deep learning will undergo evolution to turn into self-acting entities capable of autonomously detecting issues, creating appropriate test cases without assistance, and exhibiting dynamic adjustments in case of changes in software. As a result, all of this will lead to a drastic minimization of manual intervention, especially during maintenance.

Conclusion

AI brings the kinds of technologies and innovations to the table that have the power to completely revolutionize the manner in which we approach testing. Thanks to its integration with next-generation software testing tools, even QA teams with established processes are more than tempted to integrate AI into their systems.

Therefore, it won’t be an overstatement to say that AI is set to continue to bring in more optimizations and sophisticated capabilities to further advance the software testing process.

Citations

• Artificial Intelligence in Software Testing: https://www.semanticscholar.org/paper/Artificial-Intelligence-in-Software-Testing-%3A-and-Khaliq-Farooq/1f8364e42034ebc591dd03a79b4fd92b839ca2ce
• Artificial Intelligence in Software Testing: A Systematic Review: https://www.researchgate.net/publication/374263724_Artificial_Intelligence_in_Software_Testing_A_Systematic_Review

Veethee Dixit

I’m a Computer Science Engineer by degree and a passionate writer by choice. The credit for my profession as a web content writer goes to the knack for writing combined with a technical background.

See author's profile