Consumer expectations have increased exponentially in the last few years. Software development teams are under constant pressure to deliver high-quality apps at speed. This means faster testing cycles and quicker error resolution.
Autonomous testing leverages Artificial Intelligence (AI) and Machine Learning (ML) to accelerate testing while maintaining accuracy. This approach, also known as autonomous software testing, goes beyond traditional automation by dynamically adapting to application changes and reducing manual effort.
Increasingly, enterprises are adopting autonomous automation within DevOps pipelines to deliver faster, higher-quality releases.
As per Fortune Business Insights, AI-enabled testing that was valued at $856.7 million in 2024 is expected to reach $3,824.0 million by 2032. The figure is proof that organizations are largely investing in AI for software testing.
In this blog, we’ll see what autonomous software testing is and how it can help you significantly reduce manual efforts and improve efficiency.
What Is Autonomous Testing?
Autonomous testing is a modern approach where AI and ML models handle test creation, execution, and management with minimal human intervention. By leveraging autonomous software testing techniques, it can generate new test cases, adapt to app changes, and optimize coverage automatically.
How Is Autonomous Testing Different from Automation Testing?
Automation testing typically executes predefined scripts to evaluate app quality. Whereas autonomous testing uses AI to adapt and optimize test cases automatically.
Here are the main points of difference between autonomous testing and automation testing.
| Aspect | Automation Testing | Autonomous Testing |
|---|---|---|
| Test creation | Testers write test cases and automation scripts. | Testers define a testing goal, and AI/ML models generate test cases based on historical data and app behavior. |
| Adaptability | Test scripts often fail when UI code or workflows change. | Autonomous agents detect UI changes and dynamically adapt test scripts. |
| Maintenance | Test scripts need to be updated manually with every code change. | Self-healing test scripts adapt to app changes without manual intervention. |
| Decision making | Primarily follows the written test steps. | Autonomous agents can prioritize tests, predict failures, and determine steps to achieve a goal. |
| Cost | Initial setup cost is low, as tests can be reused. But there’s a high maintenance cost over time due to constant script updates. | Initial investment can be high, but lower long-term costs due to reduced maintenance overhead. |
How Autonomous Testing Works
Here’s how autonomous testing typically works in the software testing cycle.
Test planning
AI can assist you in test orchestration by analyzing complex test requirements and identifying undefined or ambiguous specifications. It suggests the best test strategies for you based on historical data and prioritizes high-risk areas of the app. Plus, it allows you to automatically schedule test cases for execution.
AI algorithms analyze past test data and application behavior to suggest optimal test strategies and prioritize high-risk areas for execution.
Test creation
You can generate detailed descriptions for test cases, including requirements, specifications, and app usage data, automatically using AI-powered test automation frameworks and tools. You can even write automated test scripts in multiple programming languages via prompt-based instructions.
AI helps you create test data based on set criteria and export it in formats like XML or CSV for efficient data-driven testing. Intelligent test automation frameworks integrate with CI/CD pipelines to ensure smooth execution and reporting.
Test management
AI allows you to organize test cases into categories, such as risk, severity, impact, and reproducibility, for easier prioritization and management. This simplifies test case management and ensures efficiency by preventing duplicates even when working with bulk actions.
Test execution
AI-powered systems can autonomously execute test cases, including repetitive tasks such as regression testing. It automatically identifies broken locators caused by changes in the codebase and fixes them to keep the tests running without manual intervention.
Debugging
By examining patterns and logs, AI can classify defects, perform root cause analysis, and even localize the areas where issues arise frequently. Based on this analysis, it can propose fixes and help you speed up the debugging process.
Stages of Autonomous Testing (ASTM Model)
The ASTM (Autonomous Software Testing Model) describes six levels of testing autonomy, from fully manual to fully autonomous. Each level increases the use of AI/ML to reduce manual intervention and improve efficiency.
Adoption and implementation of autonomous testing happens in stages, or levels of autonomy, where you gradually move from manual testing to semi-assisted and finally to a fully autonomous testing mode.
This concept is inspired by the automotive industry and known as the Autonomous Software Testing Model (ASTM).
Here are the six levels of the model:
Level 0 – Manual testing
Testers perform all the testing tasks manually, including test design, execution, reporting, and maintenance. They interact with the app by physically clicking buttons and navigating through pages to find defects.
Manual testing depends largely on human skills, which means it’s also slow and prone to errors. As the app grows, manually executing and maintaining tests can become a bottleneck.
Here’s what to do
- Write test cases in spreadsheets to create input and output scenarios
- Run regression suites manually by repeating the steps after every release
- Open the app in different browsers, such as Chrome, Firefox, and Safari, to check functionalities
Level 1 – Assisted test automation
In this level, you automate test execution using automation tools. However, you still need to design and maintain test cases manually. Tools can help you automate repetitive tasks such as recording test steps and capturing screenshots.
But designing requirements and analyzing results need human oversight.
Here’s what to do
- Write test scripts to automate test execution on the login functionality using automation tools
- Use automation tools to automate unit tests and run them after every code change
- Design test scenarios manually and use automation tools to simulate users for load testing
Level 2 – Partial test automation
At this level, you depend on automation tools and frameworks for testing. However, you still need to manually update test scripts for changes in code or workflows. The automation is more advanced, but the majority of testing decisions are made by human testers.
Here’s what to do
- Build reusable automation test scripts for the form submission feature and apply them to other parts of the app, like login or registration functions
- Configure automated regression suites to run overnight
- Develop automated test scripts for cross-browser testing
Level 3 – Integrated automated testing
Reliance on AI increases at this level. The system adapts to changes in test requirements instead of just executing predefined test scripts. However, human decisions are still critical for complex test scenarios and exception handling.
Here’s what to do
- Capture screenshots and video logs of test execution using automation tools without running test again manually
- Generate synthetic data automatically instead of manually creating datasets
- Trigger regression tests automatically with every code change using automation tools
Level 4 – Intelligent automated testing
At this level, you leverage AI and ML models to generate test cases, prioritize them, and improve coverage without constant supervision. When app UI changes, tests can self-heal and adapt to different environments. But humans still validate AI decisions.
Here’s what to do
- Automatically generate regression tests when a new feature is added
- Use ML to predict risky areas of the app and run targeted tests
- Receive real-time insights on test results
Level 5 – Autonomous testing
Autonomous agents control the testing process end-to-end, including requirement analysis, test creation, execution, and reporting without human guidance. It understands your goal and determines the best possible approach to achieve it.
The system learns continuously from user behavior and improves test accuracy with every cycle.
Here’s what to do
- Use predictive analytics to forecast potential areas that can cause defects
- Simulate how users interact with the app to create test cases automatically
- Get detailed test reports, including failed tests and untested areas
Key Components of Autonomous Testing
Natural Language Processing (NLP)
NLP allows autonomous agents to understand simple language instructions and perform tasks. Instead of writing test cases in programming languages, you can simply define your goal, such as “test login functionality,” and the agents will automatically generate test cases and perform the test steps.
Predictive analytics
Predictive analytics in autonomous testing analyzes data from past tests, defect logs, and code changes to predict the areas of an app that can potentially cause errors. Based on historical data on failure, autonomous test platforms can prioritize high-risk areas to enhance test coverage.
Continuous learning
AI models continuously collect feedback from test execution results and improve strategies over time to align with your testing goals. Cognitive testing checks if the autonomous agents understand critical paths and identify areas that require attention. This helps you enhance test accuracy with every test cycle.
Benefits and Challenges of Autonomous Testing
| Benefits | Explanation |
|---|---|
| Faster testing cycles | AI testing tools allow you to generate test cases and execute tests such as functional testing, performance testing, and visual regression testing in parallel across environments, enabling continuous feedback cycles. For every new feature update, AI automatically executes regression test suites and heals broken test scripts. |
| Improved test coverage and accuracy | Autonomous testing tools can create test cases, including complex and edge test scenarios, ensuring comprehensive coverage. |
| Cost reduction | Autonomous automation helps minimize resources spent on manual test execution and maintenance. Although the initial setup can be expensive, the ROI grows as the AI system learns and adapts to your testing needs over time. |
| Increased productivity and efficiency | Instead of designing test requirements and creating test scripts from scratch, testers can use AI to create and customize scripts for multiple test scenarios at scale. By automating repetitive tasks, they can focus on high-value activities. |
| Reduced human error | Autonomous testing minimizes human involvement in the testing process and helps reduce errors such as incorrect test data or test environment configuration mistakes. |
| Quantifiable ROI | Enterprises report up to 30–40% reduction in manual test effort with autonomous testing. |
| Challenges | Explanation |
|---|---|
| Integration complexity | Organizations working with legacy systems might face compatibility issues when integrating autonomous testing into existing CI/CD pipelines. |
| AI model training | AI models may inaccurately simulate real-world scenarios and produce biased or unreliable results if not trained properly. Training AI models requires substantial computational resources, which might be difficult for organizations with limited infrastructure. |
| Complex test scenarios | AI models may not be able to anticipate all edge cases and unpredictable user behavior. Exploratory and usability testing still need human creativity and judgment to mitigate UX gaps. |
| Test data management | AI models need large volumes of accurate and realistic data for training. This can be challenging for industries that handle highly sensitive data that must be protected as per privacy regulations. |
| AI model validation | Continuous monitoring is required to ensure AI models simulate real-world scenarios accurately. |
Autonomous Testing vs. Traditional Testing
Both traditional testing and autonomous testing aim to improve software quality, but they differ in approach. Traditional testing depends on scripted tests and human oversight. Whereas, autonomous testing incorporates AI to manage and evolve tests.
| Aspect | Traditional Testing (Gaps) | Autonomous Testing (Value) |
|---|---|---|
| Human intervention | Testers create and execute test cases manually. Human oversight is needed constantly to catch defects and update test scripts. | Autonomous agents can automatically create and generate test cases so testers can shift focus from repetitive tasks to analyzing results and improving testing strategies. |
| Speed | Test data and environment require manual setup, which slows down testing cycles. | AI can extract test data automatically from user actions, databases, and APIs, and decide the steps to perform the test. This speeds up testing and feedback cycles. |
| Maintenance | Testers need to manually update test scripts every time the app goes through changes. This process is resource-intensive and more prone to human errors. | Autonomous agents can detect changes in the app and adapt test scripts, reducing constant human supervision and maintenance overhead. |
| Scalability | As your app grows, so will the test cases. It might be challenging to manually create, execute, and maintain test cases. | Autonomous testing can scale easily and run thousands of tests in parallel across multiple environments, devices, and browsers. |
| Test coverage | Since testers manually create test cases, they might miss critical edge cases that can lead to potential defects in the app. | AI systems analyze historical defect data and user interactions to test critical paths where defects are likely to arise, improving test coverage. |
| Error Detection Rate | Manual test creation may miss critical edge cases, resulting in undetected defects. | AI-driven analysis improves coverage by predicting risky areas and detecting defects automatically. |
Top 5 Autonomous Testing Tools
1. CoTester
Cotester by TestGrid is an enterprise-grade AI agent for software testing that learns your product context and adapts to your QA workflows. It allows you to create full test cases from live application URLs or JIRA stories. You can even modify test cases by manually adding or removing steps as needed via a chat interface.
CoTester is built on a multi-modal Vision Language Model (VLM) that sees and interprets your app interface, including visuals, text, and layout. It detects even major UI changes, including full redesigns and structural shifts, and self-heals test scripts mid-execution.
You can integrate CoTester directly into your CI/CD pipeline and get detailed logs, screenshots, and step-by-step results after execution, along with a live debugging feature.
2. Testim
Testim helps accelerate your app release process with faster and accurate test building. With its AI-based recording feature, you can author your tests and capture even complex actions seamlessly. It allows your agile team to participate in low-code, NLP-based test authoring, and Proprietary Smart Locators lock in elements automatically without human intervention.
Moreover, Testim offers nearly unlimited customization options, including an option to insert JavaScript wherever needed to perform front-end or server-side actions.
3. Functionize
Functionize is a testing platform that offers a collection of GenAI tools to help you test even the most complex apps. You can test apps, APIs, databases, .pdf files, and Excel sheets.
Plus, its cloud infrastructure built for AI-powered test automation allows you to scale tests easily. Functionize leverages ML-based tests that use big data to detect site updates and self-heal test scripts as the app evolves, reducing constant maintenance efforts.
4. Mabl
Mabl is a cloud-based testing platform that supports web, mobile, and API testing. It offers you the option to create your own low-code end-to-end API tests or import tests from Postman.
You can generate JavaScript snippets to handle complex testing scenarios by using natural language. Mabl improves test stability by identifying potential flakiness and enabling cloud-powered parallel testing to avoid environment overload and save time.
5. Tricentis Tosca
Tricentis Tosca features a GenAI-powered assistant, Tosca Copilot, that allows you to quickly find, understand, and optimize test assets through a chat interface. It lets you summarize complex tests into simple language, helping you gain unprecedented control of your test library.
It supports DevOps, Agile, and waterfall workflows. Moreover, you can optimize your test suite by identifying unlinked assets, unused test cases, and duplicates using the Tosca Query Language.
Best Practices for Adopting Autonomous Testing
Now that you know what autonomous software testing is, let’s look at some best practices to help you enhance your testing strategy.
Start with a pilot project
Begin with a small-scale pilot project in a controlled environment before scaling autonomous testing to more complex projects. This will help you identify challenges and fix them before full-fledged implementation.
For example, you can start experimenting with one feature of the app and run repetitive tests to measure the effectiveness of the process.
Monitor performance regularly
To ensure the accuracy of the AI models, it’s critical to monitor performance metrics such as test coverage, defect detection rate, and execution time. Tracking performance will help you detect anomalies and adjust test strategies. This is a continuous process. As the autonomous agents evolve, you must make sure they remain reliable and align with your testing goals.
Invest in training and development
It’s critical to train your team to develop the skills required to configure, monitor, and optimize autonomous testing tools. Continuous learning is essential because AI models evolve constantly, and your team must know how to adapt to the changes and adjust testing strategies accordingly.
Wrapping Up
With growing complexities in modern app architectures, ensuring accuracy in testing processes is becoming more and more critical. By now, you know human efforts alone aren’t enough to match the increasing testing needs.
Integrating autonomous testing into DevOps and QA may soon become a necessity rather than an option.
For successful implementation, a few factors such as quality test data, smooth integration into CI/CD workflows, and team skillset will be critical. And most importantly, for ethical use of user data, compliance with governance frameworks will be absolutely non-negotiable.
With AI/ML-driven autonomous testing becoming the norm, enterprises that adopt these methods early gain a competitive edge in software quality and release velocity.
This blog is originally published at Testgrid
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