How testing tools use machine learning to simplify test creation, reduce test maintenance, and speed up the development feedback loop with intelligent test selection.
Machine learning (ML) is revolutionizing software testing and quality assurance. As DevOps practices mature and CI/CD becomes more sophisticated, the vast volumes of data generated by test suites are ripe for intelligent, data-driven automation.
End-users' growing expectations for faster delivery and higher-quality software are accelerating the adoption of ML-powered testing tooling.
CloudBees knows that the more data-driven our pipelines become, the faster we can deliver value while allowing experts more time to innovate. Recognizing that software testing is one of the most significant bottlenecks for faster feedback, CloudBees empowers developers and QA engineers to intelligently select the highest value tests to run for a specific change.
Machine Learning Algorithms
Machine learning is a sub-category of artificial intelligence containing training ML algorithms to automatically improve performance on a specific task, without explicit programming.
The most common method for training ML algorithms is supervised learning. In this approach, the algorithm learns from a labeled dataset, where each example includes both the input data and the correct output. The algorithm makes predictions based on this mapping and compares its predictions to the actual labels to calculate an error rate. It then adjusts its internal parameters to reduce that error, repeating the process until it reaches a satisfactory level of accuracy.
Other training methods include unsupervised learning, where the algorithm finds patterns in unlabeled data; semi-supervised learning, which uses a mix of labeled and unlabeled data; and reinforcement learning, where the algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties.
The Convergence of Machine Learning and Software Testing Automation
Despite significant advances in test automation, several challenges continue to impede the speed of large-scale testing cycles. Even as organizations embrace shift-left practices, test suites often vary in size and complexity, causing longer execution times for more intricate tests. Still, the core goal of shift-left remains unchanged: detecting risks earlier to enable faster releases without compromising quality.
In addition to long test durations, flaky tests remain a persistent frustration for development teams. When test results are inconsistent, developers are forced to sift through large volumes of test data to uncover the root cause, often with limited visibility and weak signals to guide them.
As applications become increasingly complex and run-time decisions become more dynamic, the only solution to bloated test suites, long runtimes, and flaky tests is to deepen automation functionality. Software testing generates a large amount of data, including test cases, test results, and defects. This data is the key to evolving software testing automation, unlocking the capability to train machine learning algorithms to identify patterns and make predictions.
Top Categories for Machine Learning being used in Software Testing
While other phases in the SDLC and throughout the CI/CD pipeline have long been prioritized for efficiency, machine learning has shown its potential to improve the quality and reliability of software testing. There are three leading categories where machine learning is advancing software testing automation at warp speed.
1. Software test creation
Manual test creation is time-consuming and prone to human error. ML algorithms can analyze code patterns to automatically generate test cases, ensuring comprehensive coverage and adaptability to evolving requirements.
Machine learning algorithms are being used to identify patterns in code and generate test cases that can be used to validate the software. These testing tools can quickly and precisely generate test cases, enabling broader test coverage in significantly less time than manual methods.
2. Software test analysis & reduction
As test suites scale over time, they tend to accumulate complexity and inefficiencies. Execution times increase, flakiness becomes more prevalent, and test reliability degrades. Although each degradation may be minor in isolation, the cumulative effect can significantly disrupt developer workflows and slow down feedback loops.
Software testing tools have advanced to harness machine learning algorithms to unlock intelligent test selection and detect the likelihood of flakiness. Model training analyzes the relationship between code changes and test failures. It builds associations between changed files and which tests tend to fail. It can be thought of as an advanced frequency counting algorithm that tracks associations between failures and source code. Essentially, model training acts as a sophisticated frequency counting algorithm that helps teams focus their testing efforts on the most relevant tests.
In addition, ML-driven flakiness detection improves the precision and consistency of identifying unreliable tests. By accurately scoring the likelihood of flakiness, teams can avoid wasting resources on low-risk tests and instead focus debugging efforts on high-priority cases. This ultimately improves the efficiency and effectiveness of the overall testing pipeline.
3. Software test health & maintenance
Maintaining large test suites is resource-intensive. ML tools monitor test suite health, flagging and addressing issues proactively and in a form that is scalable. This proactive approach ensures faster failure discovery and efficient issue triaging.
Machine learning is taking this cumbersome maintenance burden off of developers, with tools helping to monitor the health of test suites. Teams can rely on ML to flag issues and fix them before they impact your developer experience. With the right toolset, you can gain insights into your test suite health for faster discovery of failures and triaging issues.
How CloudBees Machine Learning Selects Tests for Faster Code Shipment
CloudBees Smart Tests identifies the right tests to run for each build through its machine learning model. With this intelligence, you can accelerate delivery by running a much smaller set of tests throughout your software development lifecycle.
Designed to predict which tests are most likely to fail in the shortest amount of testing time,
CloudBees machine learning model validates changes faster in four steps.
ML Model Training: Every model is trained using a mass of extracted metadata from your own test results and code changes over time
Test Selection: With a trained model, you can start requesting dynamic subsets of tests for your builds. It looks at your test suite, changes in the build being tested, and environments.
Test Prioritization: The model prioritizes tests based on the factors including test execution history, test characteristics, change characteristics, and flavors.
Subset Creation: The prioritized test list is combined with the Optimization target to create a subset of tests. This cuts the prioritized list into two chunks: the subset, and the remainder.
By adopting CloudBees Smart Tests, teams can accelerate delivery by running a smaller, more targeted set of tests throughout the software development lifecycle.
Ship 5x faster with machine learning from CloudBees. See how Smart Tests can help you launch fearlessly. Request a demo today to see it in action.