ML.NET is a .NET-optimized machine learning framework that allows developers to:
1. Train custom ML models
2. Use custom input and output models for the ML process
3. Integrate AI into existing applications
4. Run predictions
Unlike generic AI services, ML.NET lets you build models tailored to your test data, improving accuracy and reducing false positives. With ML.NET, teams can predict flaky tests before they fail pipelines, generate realistic test data dynamically, and optimize test execution order based on risk factors without relying on external AI services. By training models on historical test results, performance metrics, and code changes, ML.NET transforms traditional test automation into a self-learning system that improves accuracy over time. Whether integrated into local tests or Azure DevOps pipelines, ML.NET brings many positives to your workflow while staying fully within the already existing .NET ecosystem, including enabling proactive failure and flakiness prevention.
How Machine Learning Can Work in Test Automation
The machine learning process
ML models follow a structured workflow:
Training Data Collection
Include metrics like:
Feature Engineering
Model Training
Prediction
Examples of ML Applications in Automated Testing
Flaky Test Prediction
Test flakiness refers to tests that sometimes pass and sometimes fail without any changes to the code being tested. These unreliable tests create frustration because they waste time investigating false failures and undermine confidence in testing and automated tests. Flaky tests typically occur due to timing issues, shared state between tests, environmental differences, or dependencies on external systems. They're costly. Testers spend significant time dealing with intermittent test failures rather than productive work. The most common causes include race conditions in async code, improper cleanup between tests, reliance on system time or random data, and unstable network connections. Tracking test stability metrics over time helps identify problematic tests early.
Machine learning offers a proactive solution to this problem. By analyzing historical test execution data such as pass/fail metrics, average durations of specific tests, and deviations in execution time, ML models can learn to identify which tests are likely to be flaky. These models not only help detect existing flaky tests, but also predict future flakiness as code evolves and test conditions shift. The advantage of this approach is that it's dynamic and data-driven, adapting over time as more data is collected and entered into the model for further training.
Besides this, integrating flaky test prediction into your CI/CD pipeline can bring several key benefits. First, it helps reduce false alarms, allowing teams to focus on genuine failures rather than chasing noise. Second, predicted flaky tests can be automatically flagged as flaky or deprioritized during test runs, ensuring they do not trigger unnecessary alerts.
In environments where rapid releases and continuous integration are essential, being able to proactively detect and manage flaky tests with ML.NET means fewer disruptions, faster feedback loops and better overall test process. It transforms test automation from reactive debugging into a predictive quality assurance process.
Test data generation
Smart test data generation uses machine learning to automatically create realistic test inputs, reducing manual effort while improving test coverage. Instead of relying on hardcoded values or simple randomization, ML models learn patterns from real-world data, such as production logs or valid API payloads, and generate synthetic data that mimics these patterns. For example, a model trained on e-commerce transactions could produce test orders with realistic product combinations, pricing distributions, and customer details. Features like item categories, payment method,s and regional preferences can be used to ensure the generated data maintains logical consistency. This approach is particularly useful for testing edge cases, such as invalid inputs or rare scenarios.
Machine learning test data generation has real-world applications across a wide range of domains. It can be used in API testing, for example, to automatically produce realistic request payloads that closely simulate production. Rather than relying on manually written test inputs, QA teams can enter historical API logs into an ML model and generate inputs that cover edge cases and different combinations.
In the context of UI testing, machine learning can be used to simulate user input by populating form fields with a variety of valid and invalid values, similarly to API testing. This includes testing with unusual but possible input. By training on user behavior data, the generated inputs can better reflect how users actually interact with the system.
Several possible specific industry scenarios highlight the value of this approach. For example, as mentioned, in an e-commerce platform, an ML model trained on past transactions can generate orders that reflect real shopping behaviors, like bulk purchases or discount usage. In healthcare applications, ML-generated patient data can be created for testing workflows while maintaining data integrity in fields such as age, diagnosis, and treatment. Similarly, in financial or banking systems, models can simulate transactions from multiple countries and currencies to validate compliance checks.
By tailoring test data generation to actual real-world usage patterns, QA teams can catch more bugs, reduce false positives, and better simulate product ion-like environments and their data.
In big projects with hundreds or even thousands of automated tests, running the entire test suite after every code change or every pull request is going to be time-consuming and inefficient. Traditional approaches often execute tests in a fixed or arbitrary order, regardless of their likelihood to detect issues or other factors. Machine learning introduces a more intelligent strategy: test prioritization based on risk and relevance. It can focus on test prioritization by analyzing a combination of factors. These factors can be recent code changes, historical test failures and execution time. ML models can predict which tests are most likely to fail and surface them earlier in the pipeline.
This allows QA teams to focus testing efforts where they’re most needed, catching high-risk failures earlier in the process and providing faster feedback to developers. For instance, if a developer updates a critical payment processing component, the system can automatically prioritize tests that are historically tied to similar code areas, have a higher failure rate or interact with high-risk modules. The model might also consider factors like how old a test is (newer tests may be less stable), its historical flakiness or if an older test has been refactored lately (newly updated tests may be less stable too).
This approach brings several practical benefits. First, it shortens feedback loops by running the most impactful tests earlier, so teams can catch serious issues without waiting for the entire suite to complete which can take a lot of time if our test solution is large. Second, it improves test efficiency, especially in teams that run nightly builds or pre-merge validations. Running the entire test suite and in a fixed order before any developer merge would be a very slow and inefficient process. ML model ensures that the highest risk tests are always executed first. Third, it can reduce the overall compute cost in CI/CD pipelines, particularly when full test runs are unnecessary. Additionally, by incorporating prioritization into the development process, teams create a more risk-aware testing culture where test value is continuously assessed and optimized.
Conclusion: Why ML.NET for Testing?
There are plenty of reasons to incorporate ML.NET into your testing process. It can run entirely in .NET, so you do not need to take care of external dependencies. Besides this, it works with GitHub Actions and Azure DevOps, so you can expect CI/CD integration as well. It can also be extended with TensorFlow and other popular ML frameworks so anyone using it can get access to more machine learning scenarios, such as image classification or even object detection. By leveraging ML.NET, teams can move from reactive to predictive testing and catch issues before they impact users. Whether it's predicting flaky tests before they disrupt a pipeline, generating realistic and diverse test data automatically, or prioritizing test execution based on risk, ML.NET enables smarter and faster decisions throughout the software testing lifecycle. These improvements do not just improve efficiency; they also improve confidence in the testing process while reducing false positives and ensuring test results better reflect the real health of the codebase.