Machine Learning in Test Automation: Benefits & Real Examples

QA teams face rising complexity and demand for faster releases. Businesses aim to switch to automated testing because it provides faster and more efficient outcomes than manual testing.¹ While test automation tools are valuable assets in achieving this, using Machine Learning (ML) in test automation tools enhances the QA experience considerably.

The implementation of ML is gaining pace. A recent McKinsey survey indicates that over three-quarters of organizations now utilize AI, including ML, in at least one business function, up from 55% the previous year.² We look at the use of ML in test automation, its benefits, use cases, and challenges.

What is Machine Learning in test automation?

Machine learning, a subset of artificial intelligence (see Figure 1), is the study of computer algorithms that learn through data. Machine learning in test automation refers to using machine learning algorithms and techniques to improve various aspects of test automation, such as:

Figure 1: AI & ML models

Source: OspreyData³

Test automation tools are among the most valuable assets in reducing reliance on manual tests and the manual labor of software testing. However, they nevertheless require consistent monitoring if the software changes or updates. With machine learning models, automated tests become less prone to errors, helping businesses achieve better results in a shorter time.  

Top 6 benefits of using machine learning in test automation

1. Improved test case generation 

Machine learning can be used to analyze patterns in the system under test and generate test cases automatically, reducing the need for manual test case creation. ML increases productivity and process speed by 48%. 

2. Increased test coverage 

Machine learning can identify areas of the system that are most likely to contain bugs, enabling testers to focus their efforts on these areas and increase test coverage. ML helps companies extract quality information by %60.

3. Better test case prioritization

Machine learning can prioritize test cases based on the likelihood of a bug being present, allowing testers to focus on the most critical problems first. 65% of companies planning to adopt machine learning say ML helps businesses decide mission-critical issues. 

4. Automation of repetitive tasks 

Machine learning can automate tasks such as test data generation, reducing the time, cost, and effort required for test execution. Using ML can reduce costs by 46%

5. Predictive maintenance

Machine learning can predict when test equipment will fail, allowing for proactive maintenance and reducing downtime. Predictive maintenance can reduce test failures, allowing for more robust test scripts and software testing.

6. Natural Language Processing (NLP)

Natural Language Processing (NLP) is a growing field of Artificial Intelligence (AI) and computer science that focuses on the interaction between computers and human languages.⁴ Machine Learning can be used to automatically generate test case descriptions via NLP, which can help better understand test cases and the testing process. 

A real-world example of machine learning in software testing

Test Case Generation and Prioritization

Uber: Machine Learning for Automated Test Case Prioritization

Uber uses machine learning models to optimize the prioritization of test cases for their mobile apps. The system analyzes factors like recent code changes, user interactions, and historical bug reports to prioritize tests more likely to catch critical bugs. AI-powered systems such as DragonCrawl are integrated into their CI/CD pipeline, ensuring faster releases and reducing developers’ time on manual testing.

Uber’s DragonCrawl is an AI-powered system that utilizes large language models (LLMs) to automate mobile app testing with human-like intuition.⁵ It was developed to address the challenges of scaling mobile testing across Uber’s vast ecosystem, which includes multiple languages and regions. Traditional test scripts often fail when minor UI changes occur, and maintaining those scripts across such a diverse environment is time-consuming and costly.

DragonCrawl can dynamically adjust to changes in the user interface, such as new pop-ups or button alterations, and continues testing without requiring constant manual updates. Since its launch in 2023, the system has been used to test critical flows in Uber’s Rider and Driver apps, identifying and preventing several high-priority bugs, all while reducing developer hours spent on test maintenance. DragonCrawl has also demonstrated resilience across different devices and operating systems, significantly improving the efficiency and scalability of mobile testing.

What to consider when using Machine Learning in test automation?

Automated visual testing (UI) 

The human eye might fail to notice some problematic UI aspects. Using machine learning’s picture recognition to find and validate UI issues will be beneficial. 

An example of this would be an e-commerce website. The automated visual testing process would take screenshots of the website’s various pages and compare them to reference screenshots taken during the design and development phase. The machine learning algorithm would then identify any visual differences between the two sets of screenshots, such as misplaced buttons or text, and flag these as potential defects.

API testing 

You can record API operations and traffic using test automation tools via machine learning algorithms. This will help you evaluate and develop tests thoroughly. 

An example of machine learning in API operations and traffic is using an anomaly detection algorithm to identify unusual patterns in the API traffic. By training a machine learning model on historical data of API traffic, the model can learn the standard traffic patterns and flag any new patterns that deviate significantly from the norm.

Additionally, machine learning can be used in API traffic management by identifying the requests that take longer than usual to complete and taking action to address them

RPA for Regression testing 

RPA (Robotic Process Automation) can be used to automate regression testing. It can automate repetitive and time-consuming tasks, such as data entry and test case execution.  As a result, it can help reduce the time and resources required for regression testing. 

One example of using RPA for regression testing is automating re-running a suite of test cases after a new software release. The RPA software can be configured to automatically pull the latest version of the application from a source code repository, deploy it to a test environment, and then execute a predefined set of test cases. The RPA software can also automatically compare the results of the test cases to the expected results and generate a report of any discrepancies.

Unit tests

Using machine learning for unit tests can help improve the efficiency and effectiveness of the testing process by reducing the time and resources required for test case generation, maintenance, and execution.

One example of using machine learning in unit testing is using a neural network to generate test inputs that cover a variety of scenarios. The neural network can be trained on a codebase dataset and use the code’s understanding to generate test inputs likely to reveal defects or edge cases.

Another example is using machine learning to predict the likelihood of a unit test failure. By training a machine learning model on historical data of unit tests and their corresponding results, the model can predict the likelihood of a new test failing. 

Challenges of using Machine Learning in test automation

1. Data availability and quality: Machine learning algorithms require large amounts of high-quality data to train on. If the data is unavailable or is of poor quality, it can be challenging to train accurate models for test automation.
2. Complexity: Machine learning models can be complex and difficult to understand, making it challenging to debug or interpret their behavior for automation testing.
3. Overfitting: It occurs when a model is trained too well on the training data and performs poorly on new data. This can happen if the model is too complex or lacks enough data to train on.
4. Maintenance: Machine learning models must be retrained and updated as the system under test changes. This requires ongoing maintenance and monitoring, which can be time-consuming.
5. Integration: Integrating machine learning models into existing test automation frameworks can be challenging, requiring significant development effort.
6. Explainability: Some machine learning models may be hard to explain, which can be a problem when understanding how the model arrived at its predictions.
7. Bias: Inadequate data or preprocessing, the model can be biased and give inaccurate results.

Using machine learning in test automation requires an investment in data, resources, and expertise. It also requires ongoing maintenance and monitoring to ensure that the models continue to perform well. However, in the long run, businesses can benefit from machine learning, especially when it is incorporated into a test automation tool. 

Machine Learning & Test Automation Frequently Asked Questions (FAQs)

External Links

• 1. A Journey from Manual Testing to Automated Test Generation in an Industry Project | IEEE Conference Publication | IEEE Xplore.
• 2. The State of AI: Global survey | McKinsey. McKinsey & Company
• 3. A Journey from Manual Testing to Automated Test Generation in an Industry Project | IEEE Conference Publication | IEEE Xplore.
• 4. Global natural language processing market 2017-2025| Statista. Statista
• 5. 🐉 DragonCrawl: Generative AI for High-Quality Mobile Testing | Uber Blog.