How testing tools use machine learning to simplify test creation, reduce test maintenance, and speed up development feedback loop with intelligent test selection
Machine learning is powering the next wave of software testing and quality assurance. DevOps culture and the maturation of CICD has advanced software testing to be ripe for more intelligent pipelines powered by the tsunami of data coming from test suites.
The continuous demand for quality products and faster delivery by end-users is driving the increased adoption of ML-powered software testing tooling.
Launchable knows the more we can make our pipelines data-driven, the more we can speed up delivery while allowing experts more time to innovate. With software testing being one the most significant bottlenecks for faster feedback, Launchable is empowering developers and QA engineers to intelligently select the highest value tests to run for a specific change.
Machine learning is a sub-category of artificial intelligence containing training ML algorithms to automatically improve performance on a specific task, without explicit programming.
There are several ways to train machine learning algorithms, but the most common approach is supervised learning. The supervised learning algorithm trains on a labeled dataset and the correct output for every dataset example is given. The algorithm makes predictions based on this input-output mapping, and the predictions are then compared to the true labels to calculate an error rate. The algorithm's parameters are then adjusted to minimize the error, and this process is repeated until the algorithm reaches a satisfactory level of accuracy.
Other approaches to training machine learning algorithms include unsupervised learning, semi-supervised learning, and reinforcement learning.
In spite of our progress with testing automation, several factors continue to impede the speed of larger testing cycles. Despite organizations' efforts to shift left, test suites vary in load size and complex tests take longer. But the goal of shift left still prevails - earlier risk detection means faster releases without sacrificing quality.
While long test times tax resources, flaky tests are a prevalent plague for development teams. When flaky tests are suspected, developers have to sift through large volumes of testing data to identify the source, frequently running up against not enough clear signals.
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.
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.
Creating tests manually can be a time-consuming process, especially for large and complex systems. Human error is a common issue in manual test development, and tests may be incomplete, incorrect, or miss edge cases.
It is difficult for humans to thoroughly test all possible scenarios and combinations of inputs, and manual testing may not provide complete coverage of the system. Manual testing is inflexible, as it is difficult to quickly adapt tests to changing requirements or modify them to address new defects that are discovered.
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 generate test cases faster and more accurately than humans, allowing for more complete test coverage in a shorter amount of time.
Testing suites grow over time, and with them disorder: execution times increase, tests become more flaky. While each negative change is small and incremental, they add up and significantly impact developers.
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.
By machine learning also increasing the accuracy and repeatability of flaky test detection, teams waste less resources chasing after tests with lower flakiness scores and can prioritize critical tests that indicate higher likelihood of flakiness.
Software testing requires substantial maintenance and is the bane on developer resources. Over time, the number of tests in the test suite may grow, leading to increased maintenance overhead and slower test execution times. Tests that are not well-designed or that have a high degree of coupling to the system under test may be prone to breaking when changes are made to the code, requiring frequent maintenance.
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.
Launchable’s Predictive Test Selection 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, Launchable’s 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
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.
The model prioritizes tests based on the factors including test execution history, test characteristics, change characteristics, and flavors.
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.