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Machine Learning to Predict Test Failures

With the introduction of Artificial Intelligence and Machine Learning, technology has advanced significantly. We are currently using machine learning algorithms to predict failures in test execution. This is achieved by developing and deploying models that learn the historical test execution result and then expect the potential shortcomings in the application. With this approach, we can catch the defects early before deploying them into the production environment. This will reduce significant costs and time and also help improve customer satisfaction.

Let’s understand more about ML and how it helps predict failures during software testing.

What is Machine Learning?

Machine Learning (ML) is a branch of Artificial intelligence (AI) that focuses on designing algorithms to identify patterns within datasets. These algorithms learn from the data, allowing them to predict new, similar data types without being explicitly programmed for specific tasks. Unlike traditional programming, where rules and logic are explicitly coded by humans, machine learning enables computers to learn and make predictions based on their data experiences.

Here are the core concepts of machine learning:

  • Learning from Data: Machine learning involves feeding data to algorithms to help them learn and make informed predictions or decisions. For instance, showing a machine learning model numerous images of cats and dogs will help it learn to distinguish between the two.
  • Types of Machine Learning:
    • Supervised Learning: This involves training a model on a labeled dataset, where the correct answers (labels) are provided, and the model learns to predict the labels from the data features.
    • Unsupervised Learning: Here, the model works on unlabeled data to find patterns and relationships in the data itself, such as clustering similar customers in marketing data.
    • Reinforcement Learning: This type of learning uses a system of rewards and penalties to compel the model to learn by itself through trial and error within a defined set of actions.
  • Models and Algorithms: Machine learning employs a variety of algorithms and models, including decision trees, neural networks, support vector machines, and many others, each with its own strengths and ideal use cases.
  • Training and Testing: The process typically involves dividing the data into training and testing sets. The model learns from the training set and then evaluates how well it predicts new, unseen data from the testing set.
  • Applications: Machine learning is used across many fields, including finance for credit scoring, healthcare for disease prediction, tech for voice recognition systems, marketing for customer segmentation, and autonomous vehicles for driving.

ML in Software Testing

Machine learning (ML) can be a powerful tool in software testing, offering a range of benefits and innovative approaches to improve testing efficiency and effectiveness. Here are some key areas where machine learning is making an impact:

  • Test Case Prioritization: ML algorithms can prioritize test cases based on their historical data on test results, changes in code, and other factors. This helps in running the most critical tests earlier, which can be especially beneficial in continuous integration/continuous deployment (CI/CD) environments.
  • Defect Prediction: ML models can predict the likelihood of defects in various parts of the software by analyzing past commit data and test results. This allows testers to focus their efforts strategically and catch potential bugs early in the development cycle. Read: Minimizing Risks: The Impact of Late Bug Detection.
  • Automated Test Generation: Machine learning can help in generating test cases automatically based on requirements and user stories. This includes generating inputs for the system and predicting the expected outputs, thus helping in expanding test coverage without significant manual effort.
  • Visual Testing: ML techniques, especially those involving computer vision, can automate the process of visual validation of GUI applications by comparing screenshots taken during testing with baseline images. Know How to do visual testing using testRigor?
  • Anomaly Detection: In performance testing, machine learning models can identify patterns in application performance data that deviate from normal behavior. These patterns might indicate issues like memory leaks, resource bottlenecks, or other performance problems.
  • Natural Language Processing (NLP): ML models employing NLP can interpret and understand test cases written in natural language, aiding in the automation of converting these test descriptions into executable test scripts.
  • Flaky Test Detection: Flaky tests exhibit inconsistent results, passing and failing over different runs without changes to the code. ML can analyze test execution logs to identify and isolate flaky tests.

How ML Predicts Test Failures

Machine learning algorithms can analyze vast amounts of data to detect patterns and make predictions, making them well-suited for identifying potential test failures in software applications. By integrating ML into the testing process, organizations can anticipate problematic areas, optimize testing efforts, and improve software quality. The predictive capability of ML models can help prioritize testing resources, target high-risk components, and reduce the manual effort required in testing procedures.

Let’s go through the various aspects of using machine learning in this context, including its methodology, benefits, challenges, and practical applications.

Step 1: Data Collection and Preprocessing

The first step in applying machine learning to predict software test failures is data collection. Data relevant to testing processes typically includes:

  • Test Execution Data: These contain the data from the execution of previous tests (e.g., pass, fail, error) and execution logs. Read this informative Test Log Tutorial for more details.
  • Code Changes: This data mainly contains information on code commits, including lines of code changed and files affected.
  • Historical Metrics: The previous bug reports, developer comments, and other metrics related to the test environment.

Accurate data collection is crucial as the quality and quantity of data directly affect the model’s performance.

Step 2: Feature Engineering

Once data is collected, the next step is feature engineering, which involves selecting and transforming raw data into features that effectively represent the problem to the predictive model. In the context of predicting test failures, relevant features might include:

  • Code Complexity Metrics: Such as cyclomatic complexity, which could correlate with the likelihood of bugs.
  • Change Frequency: Modules that undergo frequent changes might be more prone to errors.
  • Developer Experience: Experience level of the contributors making changes to the codebase.

Feature engineering is both an art and a science, requiring domain knowledge and analytical skills to identify features that significantly impact software test outcomes.

Step 3: Model Selection and Training

Choosing the right ML model is critical. Various models can be used, including logistic regression, decision trees, random forests, support vector machines, and neural networks. The choice of model often depends on the size and type of data available, the specific prediction task, and the desired accuracy and interpretability of the model. Training involves using historical data to teach the model to predict outcomes based on input features.

This step requires a careful balance of parameters to avoid overfitting, where the model performs well on training data but poorly on unseen data.

Step 4: Validation and Testing

After training, the model must be validated and tested to ensure it generalizes well to new data. This typically involves:

  • Cross-Validation: Using separate data subsets to train and test the model multiple times to assess its stability and reliability.
  • Performance Metrics: Evaluating the model using metrics like accuracy, precision, recall, and F1-score to gauge its effectiveness in predicting test failures.

These steps are crucial to confirm that the model performs reliably and does not overfit the training data.

Step 5: Integration and Deployment

Integrating the ML model into the existing software testing environment is a complex but transformative step. Deployment involves setting up the model to receive new test data, predict outcomes, and provide insights on the fly. This integration often requires adjustments in the testing workflow, such as adding new processes to handle model predictions and feedback.

Step 6: Continuous Improvement

The deployment of an ML model is not the final step; continuous monitoring and improvement are necessary to maintain and enhance its performance. This involves:

  • Regular Updates: Retraining the model with new data to adapt to changes in the software and testing environments. Read What is Continuous Testing?
  • Feedback Loops: Incorporating feedback from actual test results to refine the model predictions.

AI/ML Integrated Automation Tools

AI and ML automation tools have brought significant advancements over traditional automation technologies, offering enhanced capabilities, improved efficiency, and the ability to handle more complex tasks. Currently, automation tools use AI/ML to generate test data and predict test failures. Let’s take an example of a modern intelligent test automation tool like testRigor.


testRigor is a codeless test automation tool with generative AI integration. With its AI/ML capabilities, testRigor offers many features that currently most automation tools can’t offer.

Let’s look into those.

  • AI-powered Test Generation: Using testRigor’s generative AI, you can generate test cases or test data by providing a description alone. This helps to cover more edge case scenarios and also helps to find potential bias or any unexpected issue that standard testing may not catch.
  • Natural Language Automation: testRigor stands out by enabling users to write test scripts in parsed plain English, eliminating the need for coding expertise. You just need to write the script in English, then With its Natural Language Processing(NLP), convert those English steps into testRigor understandable language and execute the test. This improves the test case coverage, thereby covering more testing scenarios, finding more bugs, and making the application more stable.
  • Stable Element Locators: Unlike traditional tools that rely on specific element identifiers, testRigor uses a unique approach for element locators. You simply describe elements by the text you see on the screen, and the ML algorithms do the rest for you. This means your tests adapt to changes in the application’s UI, eliminating the need to update fragile selectors constantly. This helps the team focus more on creating new use cases than fixing the flaky XPaths.
    Here is an example where you identify elements with the text you see for them on the screen.
    click "cart"
    click on button "Delete" below "Section Name"
  • Visual Testing: testRigor helps to execute visual testing on your application. So, with testRigor, you can compare the screens or elements from the previous execution and check if there is any deviation. This is done with the help of ML algorithms. With testRigor’s visual testing, we can ensure all the UI elements are loaded correctly on the page.

Read this guide to learn more about How to do visual testing using testRigor?

Let’s review a sample test script in testRigor, which will give more clarity about the simplicity of test cases:
login as customer //reusable rule
click "Accounts"
click "Manage Accounts."
click "Enable International Transactions"
enter stored value "daily limit value" into "Daily Limit"
click "Save"
click "Account Balance" roughly to the left of "Debit Cards"
check the page contains "Account Balance"

As you can see, no complicated XPath/CSS locator is mentioned, and no complex loops or scripts are required. Just use plain English or any other natural language, and you will be ready with intelligent test automation. Here are the top features of testRigor.


With the help of machine learning algorithms, we can predict the application’s failures earlier. This helps us save a lot of time and also helps us decide which application area we need to put more effort into. Using smart automation tools like testRigor, which is powered by AI, ML, and NLP, reduces a lot of unnecessary work and also increases the efficiency of test automation.

By leveraging the power of tools like testRigor, we can ensure the bugs are caught early, and we can support more frequent releases, which has become the mantra for software companies.

Additional Resources

Frequently Asked Questions (FAQs)

Are there any tools that integrate machine learning for test automation?

Yes, testRigor utilizes machine learning to identify element locators. Users don’t have to give XPath. Instead, they provide the text they see on the UI or the relative position on the screen, and the ML algorithms do the rest.

What are the challenges in using machine learning to predict test failures?

Challenges include collecting and maintaining a high-quality dataset, choosing the right features and model, integrating the ML model into the existing testing workflow, and ensuring the model remains accurate over time as the software evolves.

Can machine learning completely replace human testers?

Machine learning is beneficial in testing because it can predict when something might go wrong and help plan the tests better. However, it can’t do everything that human testers do. Humans are still needed to understand the results, make complicated choices, and develop unique test situations that a machine might not think of. So, while machine learning is a great help, it doesn’t replace the need for human testers.

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