Testing is a crucial activity in the speed-focused development culture of the modern era, ensuring code quality while speeding up the delivery rate. Yet, test flakiness, where tests yield inconsistent results without any change in the underlying code, is a chronic issue eroding confidence in automation while hindering development cycles.
Conventional ways of dealing with flaky tests tend to use manual triage or basic searching, which are prone to error and less efficient. That is where AI test tools and custom machine learning (ML) models come in as a strong solution. By leveraging historical test data and intelligently learning patterns of instability, custom ML models can predict which tests are likely to be flaky before they cause disruptions.
This article explores how building tailored ML models specifically for test flakiness prediction can transform testing pipelines. It will dive into understanding flaky tests, their causes, and the role of AI test tools in predicting test flakiness. We will also understand custom ML models, their need for predicting test flakiness, and some tips.
Understanding Flaky Tests
Flaky tests are the ones that yield peculiar results, occasionally passing and occasionally failing without requiring adjustments to the environment, app, or code. The tests are less pleasant because they give mixed feedback, so it is hard to tell the difference between a problem with the app and with the test. Device fragmentation (due to variations in hardware, screen sizes, and operating systems), network instability, and UI animations can produce flaky tests.
Additionally, dynamic element locators that vary with each session, timing, and synchronization problems (such as waiting for elements to load) are some of the other factors that frequently lead to flaky tests in mobile automation.
Causes of Flaky Tests
Some causes of flaky tests are mentioned below:
- Problems with synchronization: Slow-loading features, animations, and transitions are common in mobile applications. The test might not work if it attempts to interact with these before the elements are accessible.
- Unreliable Network Conditions: Some problems, like bad Wi-Fi, can occur because of fluctuating network conditions if the test depends on the network and on timeouts.
- UI Rendering and Interactions: Flakiness of UI-related tests can be caused by UI rendering inconsistencies or inconsistent interactions owing to the disparities between browser versions or screen resolutions.
- Asynchronous Operations: Sometimes, flakiness arises because of problems with the timing or because there are failures to conduct tests that involve asynchronous operations like AJAX requests, causing the results to be unpredictable.
- Caching and State Management: Tests might become flaky because outdated data cache or session states are employed instead of fresh ones, which may result in inconsistencies in the data retrieval or state management mechanism.
What are Custom ML Models?
Custom ML models are tailor-made machine learning models purposefully designed, trained, and tuned to meet the particular requirements and data of a specific problem or organisation rather than applying pre-packaged generic models. In contrast to pre-packaged models with predetermined features and behaviour, custom ML models are built from the ground up or tailored to take advantage of an organisation’s data, business rules, and objectives.
Role of AI and ML in Predicting Test Flakiness
- Learns from Test Run History- Based on historical test run data, such as success/failure status, execution time, system load, and environment details, AI platforms are able to identify trends and patterns associated with flaky behavior.
- Forecasts Flakiness Prior to Run- With the model’s comprehension of test history and activity, AI can forecast the probability of a test being flaky for future runs. This allows teams to make savvy decisions regarding test priorities or reruns.
- Reduces False Failures in CI/CD Pipelines- False alarms from flaky tests commonly break builds. AI tools minimize this noise by isolating or deprioritizing potential flaky tests, enabling more consistent build and deployment cycles.
- Supports Root Cause Analysis- AI can group similar test failures and analyze corresponding metadata to highlight potential causes, such as environmental instability, data race conditions, or resource issues, simplifying troubleshooting.
- Boosts Trust in Automation- AI tools reduce flakiness and enhance the accuracy of tests, thereby empowering teams to regain trust in automated testing. Such confidence is instrumental in teams that work on continuous integration and delivery.
- Manages Sophisticated Testing Scenarios- Generic, rule-based tools are ineffective when it comes to complex, dynamic test environments. Here, custom ML models come to the rescue.
- Gives Total Control Over the Model- Teams get total control over feature engineering, model selection, training schedules, thresholds, and how predictions are integrated into CI/CD pipelines.
- Improves Overall Testing Reliability- With accurate, context-specific predictions, tailored ML models assist in delivering more reliable test results, making test automation more reliable and actionable.
Tips for Building Custom ML Models for Test Flakiness Prediction
Listed below are some tips for building custom ML models for test flakiness prediction:
Engineer Interesting Features- Get raw data converted into useful input for the model. Features can be how frequently a test fails, fluctuation in test running time, trends in test running order, environment details (such as specific OS or browser versions), or retry counts based on history. Informed feature engineering makes the model understand the subtle conditions that lead to flakiness.
Treat Data Imbalance with Care- Flaky tests typically constitute a minority of all tests and form an unbalanced dataset. To avoid the model skewing towards the majority class (stable tests), use methods like oversampling flaky examples, undersampling stable tests, employing synthetic data generation techniques like SMOTE, or balancing class weights during training in order to assign more weight to flaky cases.
Select the Appropriate Model Type- Start with basic, explainable models such as logistic regression or decision trees to have a reference point and make initial feature impact estimates. Depending on dataset size and complexity, move to more powerful models such as random forests, gradient boosting (XGBoost, LightGBM), or neural networks for increased precision, particularly when dealing with complex, high-dimensional data.
Split Data Strategically- Don’t make random splits that could leak future information into training. Instead, utilize time-based splits that mimic actual deployment scenarios, train on older test data, and test on newer runs. Alternatively, stratify splits to preserve flaky/stable ratios between train and test sets, guaranteeing fair evaluation.
Embed into CI/CD- Incorporate the model’s predictions into your continuous integration pipeline. Apply flakiness scores to mark tests for possible flakiness prior to running, quarantine potential flaky tests, prioritize stable ones for early execution, or initiate auto-retries. Smooth integration maximizes the model’s real-world value to build stability and developer productivity.
Visualize and Track Forecasts- Create dashboards that show flaky test results, historical trends, model quality metrics, and flagged tests. Visualisation allows QA teams and developers to comprehend patterns of flakiness, monitor progress, and make rational decisions on test maintenance or debugging focus.
Partner with QA and Dev Teams- Work hand-in-hand with developers and testers to narrow down labeling rules, choose useful features, and check model outputs. Their expertise within a domain is essential for understanding predictions and making the model more practical in real-world applications.
AI Test Tools for Building Custom Models to Detect Test Flakiness
Below are top tools used to build custom ML models tailored for flaky test prediction:
KaneAI by LambdaTest- Unlike conventional solutions that excel at creating machine learning models from scratch, KaneAI by LambdaTest provides a pre-packaged, AI-driven solution for enhancing the reliability of tests, which specifically includes detecting and preventing flaky tests. Although it doesn’t offer a platform for developing bespoke ML models, KaneAI uses AI methods internally to scan test patterns, expose inconsistencies, and mark possible flakiness through in-built test intelligence.
By integrating test analytics with natural language processing (NLP), KaneAI drastically reduces the entry threshold for teams seeking to integrate AI into test maintenance, particularly in detecting flaky tests. Additionally, KaneAI complements other QA tools, including accessibility testing tools, by providing a unified environment for intelligent, automated, and inclusive test execution and analysis.
Features:
- Automatically detects conflicting test outcomes and flags probable flaky tests based on execution history and behavioral trends.
- Provides dashboards and insights into test flakiness trends, pass/fail rates, and root-cause correlations.
- Supports creation and debugging of tests through natural language, reducing manual scripting.
- Suggests or enforces automatic resolution of flaky behaviour-affected tests.
- Works with Selenium, Cypress, Playwright, Appium, and others.
- Works seamlessly with Jenkins, GitHub Actions, and other CI tools to monitor continuous flaky behaviour.
TensorFlow- Google created this open-source ML framework. It’s extremely flexible and excellent for developing deep learning models able to identify complicated patterns within test flakiness. Its scalability and strong ecosystem make it perfect for massive CI/CD pipelines.
Features:
- Supports deep learning as well as traditional ML models.
- Has tools for training, evaluation, and deployment of models.
- Integrated TensorBoard for model visualization.
- Scalable on CPUs, GPUs, and TPUs.
- Strong community and documentation.
Scikit-learn- Scikit-learn is a Python package dedicated to classical machine learning. It’s great for creating rapid prototypes and trying out features such as decision trees, random forests, and logistic regression to identify test flakiness.
Features:
- Great for feature engineering and model evaluation.
- Offers numerous supervised and unsupervised algorithms.
- Integrates well with NumPy and pandas.
- Lightweight and fast for small to medium datasets.
Apache Spark MLlib- Spark MLlib is a scalable ML library built on Apache Spark. It is excellent for analyzing large volumes of test data, especially in distributed systems, making it perfect for enterprise-level flakiness detection.
Features:
- Distributed processing of large-scale datasets.
- Supports parameter tuning and model pipelines.
- Supports several ML algorithms (SVMs, decision trees, etc.).
- Easy integration with big data tools.
MLflow- MLflow is a tool for managing the complete ML lifecycle, including experimentation, reproducibility, and deployment. It helps track flaky test prediction models from development through production.
Features:
- Model versioning and experiment tracking.
- Easy integration with scikit-learn, TensorFlow, and Spark.
- Offers model packaging and deployment tools.
- Enables collaboration and reproducibility.
- Supports both local and cloud storage backends.
Pandas + Jupyter Notebooks- Although not ML tools in themselves, pandas and Jupyter Notebooks are a must for test data preprocessing, exploratory data analysis, and rapid prototyping. They offer an extensible and interactive environment to examine flaky test behaviour.
Features:
- Powerful data manipulation and cleaning capabilities.
- Rich visualizations with matplotlib and seaborn integration.
- Interactive notebook interface for rapid experimentation.
- Supports inline documentation and collaborative workflows.
- Ideal for early-stage model development.
Launchable- Launchable uses machine learning to predict which tests are most likely to fail based on code changes and historical test data. While it doesn’t allow building custom ML models, it offers powerful flaky test detection and prioritization through intelligent test selection.
Features:
- Predictive test selection to speed up CI pipelines
- Highlights historically flaky tests
- Analyzes code-to-test impact
- Supports major frameworks (JUnit, PyTest, etc.)
- Integrates with CI tools like GitHub Actions, Jenkins
- Complements accessibility testing tools by reducing noise in test results
Conclusion
In conclusion, developing bespoke ML models for test flakiness prediction enables teams to detect flaky tests ahead of time, minimize false failures, and enhance the reliability of auto-testing. With personalized data and intelligent algorithms, such models enable more insightful test management and frictionless CI/CD pipelines. The more complex software systems become, the more necessary the use of AI-based solutions in detecting flakiness will be for the purpose of ensuring high-quality, efficient development cycles.
