If you’ve automated test cases, you’ll know that it can be quite frustrating when your tests fail, either randomly or because of some unforeseen issue. You end up spending your resources to go back and trace the source of the issue, trying to fix it. Quite often, these efforts feel in vain because some other issue pops up, especially if you’re using traditional test automation tools that do not harness artificial intelligence (AI). This makes the entire approach rather reactive and time-consuming. 

But what if you could use tools to predict where issues would arise? Wouldn’t this help you to build a robust testing system that can anticipate such surprises in advance? 

By using AI and machine learning (ML), you can achieve this. Let’s understand how. 

Reasons for Test Failures

When it comes to automated testing, there are many reasons why the test failed.

  • Incorrect Test Data: The test is using the wrong data to check the application, which leads to failure.
  • Changes in the Application: When the app or website is updated (like code changes, UI redesigns, or new features), it can break the automated test if the test is looking for something that no longer exists or works the same way.
  • Test Script Errors: Sometimes, the test itself is written incorrectly or doesn’t handle certain scenarios well, which can lead to failure.
  • Environment Issues: The test fails because the environment (where the test runs, such as a server, database, or network) isn’t set up properly or is unstable.
  • Timing or Synchronization Problems: Automated tests can fail if they don’t wait long enough for an action to finish or if the test moves too quickly through the application.
  • Browser or OS Differences: The test might work on one browser or operating system but fail on another because browsers or operating systems can behave differently.
  • Flaky Tests: These are tests that sometimes pass and sometimes fail without a clear reason. It’s like flipping a coin – it could go either way.
  • Unreliable Network or Server Issues: If the test needs to fetch data from a server or depends on the network, a problem like a slow or disconnected network can cause the test to fail.
  • Memory or Resource Limitations: This happens when the system running the automated tests runs out of resources, such as memory or processing power. This can cause tests to fail.
  • Human Error in Test Setup: Sometimes, the test fails because the person setting up the test did something wrong, such as choosing the wrong test case or configuring the environment incorrectly.
  • Test Dependency Failures: Some tests depend on the successful execution of other tests. If one test fails, it can cause others to fail as well.

Test Failures Predictions: The ML Approach

Imagine having a crystal ball that could peek into the future of your test suite and tell you which tests are most likely to fail before they actually do. While we don’t have magic, ML offers remarkably similar capabilities. At its core, ML empowers computers to learn from data without being explicitly programmed. In the context of predicting test failures, we feed historical information about our tests, code changes, and development environment into these learning algorithms. The goal? To train a model that can identify patterns and relationships that signal an increased risk of a test failing in the future.

We provide ML with a vast dataset of past test executions, along with relevant contextual information, and the algorithm learns to “recognize” the characteristics associated with test failures.

Key stages for applying machine learning to predict test failures:

  • Data Collection: You might know that for ML to do its job, it needs high-quality data. This might mean historical test results (pass/fail status, execution time), details about the code changes leading up to the test run (which files were modified, who made the changes, commit messages), information about the testing environment (browser versions, operating systems, configurations), and even data from bug reports related to specific tests or code areas. The more comprehensive and clean our data, the better our model will be at making accurate predictions.
  • Feature Engineering: Raw data, while essential, often needs to be transformed into a format that machine learning models can understand and learn from effectively. This process is called feature engineering. It involves extracting, transforming, and selecting the most informative pieces of information (features) from our collected data. Effective feature engineering is often the secret sauce that significantly boosts the performance of a predictive model.
  • Model Selection: Once we have our features ready, the next step is to choose an appropriate ML model. There isn’t a one-size-fits-all model; the best choice depends on the nature of our data and the patterns we’re trying to uncover. For predicting whether a test will pass or fail, we often turn to classification models. These models are designed to categorize data into distinct classes – in our case, “Pass” or “Fail.” Examples of popular classification models include Logistic Regression (good for interpretability), Decision Trees and Random Forests (powerful for capturing non-linear relationships), Gradient Boosting Machines (known for high accuracy), and Support Vector Machines (effective in complex scenarios).
  • Model Training: This is where the machine learning algorithm learns from the historical data. We feed the model our training dataset, which contains examples of past test runs along with their outcomes (pass or fail). The model analyzes this data, identifies the relationships between the features and the outcomes, and adjusts its internal parameters to make accurate predictions on unseen data. Think of it as the model learning the “rules” that govern test failures based on the historical evidence.
  • Model Evaluation: After training, we need to evaluate how well our model performs. We use a separate set of data that the model hasn’t seen before (the test set) to assess its predictive capabilities. We look at various metrics to determine how accurately the model can predict failures. This evaluation step is crucial for making sure that our model is reliable and provides real value.
  • Deployment and Monitoring: Once we have a well-performing model, the final step is to deploy it into our development workflow. This could involve integrating it with our continuous integration/continuous delivery (CI/CD) pipeline to provide real-time predictions as code changes are made and tests are executed. We might visualize these predictions on dashboards or set up alerts to notify development teams of potentially failing tests. Crucially, we also need to continuously monitor the model’s performance over time and retrain it with new data to ensure its accuracy remains high as our codebase and testing environment evolve.

Key Data and Feature Engineering for Prediction

The success of any ML model hinges on the quality and relevance of the data it learns from. When it comes to predicting test failures, we need to identify and collect various types of information that could potentially signal an increased risk of a test failing.

Key Data Sources

  • Test Execution History: This is arguably the most direct source of information about test outcomes. It includes:
    • Test Case ID/Name: Unique identifier for each test.
    • Execution Timestamp: When the test was executed.
    • Pass/Fail Status: The outcome of the test execution.
    • Execution Duration: How long the test took to run. Trends in execution time can sometimes indicate underlying issues.
    • Error Messages and Logs: Detailed information about why a test failed. This textual data can be a goldmine for identifying patterns.
    • Test Environment Details: Information about the specific environment in which the test was run (e.g., browser version, operating system, database version, specific configurations).
  • Code Commit History: Changes to the codebase are a primary driver of test failures. Analyzing commit history can reveal potential risk areas:
    • Commit ID/Hash: Unique identifier for each commit.
    • Author: The developer who made the changes. Experience levels or specific developers working on sensitive areas might be relevant.
    • Commit Timestamp: When the changes were committed.
    • Files Changed: The specific files that were modified in the commit.
    • Commit Messages: The description of the changes made. Natural Language Processing (NLP) techniques can extract keywords or sentiment that might indicate risky changes (e.g., “bug fix,” “refactoring,” “critical”).
  • Bug Reports and Issue Tracking Data: Information about previously reported bugs can provide valuable context:
    • Bug ID: Unique identifier for each bug.
    • Reported Date: When the bug was reported.
    • Resolution Date: When the bug was fixed.
    • Severity/Priority: The impact of the bug.
    • Affected Components/Files: Which parts of the system were affected by the bug.
    • Bug Description: The detailed description of the issue. NLP can be used here as well.
    • Link to Commits: If bug fixes are linked to specific commits, this provides a direct connection.
  • Static Code Analysis Results: Tools that analyze code without executing it can identify potential issues:
    • Number and Types of Warnings/Errors: Metrics on code quality and potential vulnerabilities.
    • Affected Files/Lines of Code: Pinpointing areas with potential problems.
    • Severity of Issues: Indicating the seriousness of the identified problems.
  • Infrastructure and Environment Metrics: Issues in the testing or deployment environment can lead to test failures:
    • Resource Utilization (CPU, Memory, Disk): High resource usage might indicate performance bottlenecks.
    • Network Latency: Issues with network connectivity can cause intermittent failures.
    • Deployment Information: Details about the deployed version and configuration.
    • External Service Availability: The status of dependent services.

Feature Engineering for Better Predictions

Once we’ve identified and collected our data, the crucial step is to engineer features that our machine learning models can learn from. Here are some examples of how we can transform the raw data into informative features:

  • From Test Execution History:
    • Test Failure Rate (Historical): For each test, calculate the percentage of times it has failed in the past N executions or within a specific time window. Tests with a higher historical failure rate are inherently more likely to fail again.
    • Test Execution Time Trends: Calculate the average execution time over a recent window and look for significant deviations from this average. Increasing execution time might indicate performance issues or underlying problems.
    • Recency of Last Failure: How recently a test last failed. A test that failed recently might still be unstable.
    • Frequency of Failures: The number of times a test has failed in a specific period.
    • Consecutive Failures/Passes: The number of consecutive times a test has passed or failed. A long streak of failures is a strong indicator.
    • Common Error Patterns (from logs): Using NLP techniques (e.g., keyword extraction, TF-IDF) to identify recurring error messages or patterns in the failure logs.
  • From Code Commit History:
    • Number of Recent Commits Affecting a File: For tests related to specific files, count the number of commits that have modified those files in a recent period. More changes might introduce more bugs.
    • Complexity of Recent Changes: Quantify the complexity of recent code changes using metrics like the number of lines changed or the number of affected functions. More complex changes can be riskier.
    • Developer Experience (on modified files): Track the experience level of the developers who recently committed changes to the files related to a test. Less experienced developers might introduce more issues (this requires careful and ethical consideration).
    • Keywords in Commit Messages: Extract keywords from commit messages (e.g., “fix,” “bug,” “refactor,” “critical”) and create binary features indicating their presence.
    • Time Since Last Change to a File: How recently the code related to a test was modified. Older, less frequently changed code might be more stable.
  • From Bug Reports and Issue Tracking Data:
    • Number of Open Bugs Related to a Test/Component: A higher number of open bugs in the area covered by a test might indicate instability.
    • Severity of Recent Bugs: The severity of the most recently reported bugs related to a test or its underlying code.
    • Time Since Last Bug Fix: How recently a bug was fixed in the code related to a test.
    • Frequency of Bug Reports (related to a test/component): How often bugs are reported in the area covered by a test.
  • From Static Code Analysis Results:
    • Number of High-Severity Warnings in Affected Files: A high number of critical static analysis warnings in the files related to a test could indicate potential issues.
    • Types of Static Analysis Violations: Create categorical features for the different types of static analysis warnings.
  • From Infrastructure and Environment Metrics:
    • Average Resource Utilization During Previous Failures: If past failures correlated with high resource usage, this can be a predictive feature.
    • Environment Configuration Changes: Track recent changes to the testing environment configuration.
    • Availability of External Services: The historical uptime of dependent services during test executions.

Looking Ahead

While we already have tools utilizing ML to help predict test failures, we still have a long way to go. Expect advancements in predictive accuracy through deeper data integration and sophisticated feature engineering. We’ll see increased test automation autonomy with self-healing capabilities and intelligent anomaly detection. Models will become more interpretable and offer actionable insights. Seamless integration into DevOps pipelines will enable real-time feedback and predictive quality gates. 

The focus will also narrow to predict specific failure types and aid in root cause analysis. Emerging technologies like chaos engineering and digital twins will further enhance predictive power. Ultimately, ML will empower QA teams to shift from reactive debugging to proactive prevention.