The Machine Learning Alternative to Traditional Test Impact Analysis
Traditional Test Impact Analysis has a shelf life, but machine learning is supercharging more accurate & faster test selection.
Key Takeaways
Traditional Test Impact Analysis often relies on source code analysis to develop dependency metrics between tests and code.
This common code coverage-based approach has a shelf life - but machine learning is helping to pick up where Test Impact Analysis plateaus.
Built to predict the tests most likely to fail in the shortest amount of testing time, Launchable’s machine learning model validates changes faster than traditional Test Impact Analysis in four steps.
Development teams facing the pressure to speed up release cycles often look to Test Impact Analysis for a data-driven solution. It can be a powerful method to rev up your STLC velocity, deliver developers critical feedback faster, and tee up more accurate, higher-quality releases in a speedier manner.
Traditional Test Impact Analysis often relies on source code analysis to develop dependency metrics between tests and code. This common code coverage-based approach has a shelf life - but machine learning is helping to pick up where Test Impact Analysis plateaus.
Traditional Test Impact Analysis Methods
Standard Test Impact Analysis methods use a map of dependencies to select the most critical tests to run. By identifying the relationship between test cases and code changes, this mode of static source code analysis becomes more difficult when the data you're working with grows quickly.
Another traditional method to Test Impact Analysis is to re-run all tests after a code change. This tactic can significantly bloat the run time of your test suite, and drags down testing cycle momentum. Re-running every test, after every single change can become an unnecessary and tedious activity for devs.
Test Impact Analysis can also be performed by selectively running only relevant tests, which also may introduce a higher level of risk into testing, thanks to the possibility of missing an issue. There’s also the chance that a test will not deliver the same results the next time its run, especially if it did not previously exercise certain parts of code.
Finally, one more traditional Test Impact Analysis approach is to perform a manual analysis of test results. This requires the time, effort, and expertise of devs, and can be quite costly from an organizational standpoint. But, for proponents, the human expertise applied to the task is worth it.
All in all, standard methods of Test Impact Analysis can become complex and time-consuming, especially as data sets grow. It often requires specific programming languages and tools, which also makes it a more challenging (and frustrating) mode of operating. With these limitations, traditional approaches to Test Impact Analysis bottoms out.
Related Article: Test Impact Analysis Hands-On Introduction for Faster Releases
ML-Powered Test Impact Analysis
With testing resulting in a tsunami of data, Test Impact Analysis is ripe for advancing into an automated data-driven process with the incorporation of machine learning. Predictive Test Selection is a type of Test Impact Analysis that uses machine learning to select the highest value, most important tests to run for a specific change.
Launchable built Predictive Test Selection to help developers determine which tests to run for a given code change, through a machine learning model trained on historical test results.
Built to predict the tests most likely to fail in the shortest amount of testing time, Launchable’s machine learning model validates changes faster than traditional Test Impact Analysis in four steps.
Machine Learning Model Training: Every model is trained using a mass of extracted metadata from your own test results and code changes over time. The training helps the model to detect patterns and predict the impact of code changes on test cases. Launchable uses your git commit metadata to analyze your test suites through the Launchable CLI, a process that works with existing pipelines.
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.
With Launchable and Predictive Test Selection, the automation of the analysis of test results works to give devs feedback faster, so changes can be made more quickly. Ultimately, this improves release times.
Faster, More Accurate Test Impact Analysis with Predictive Test Selection
Predictive Test Selection massively saves time and effort that would otherwise be required for manual analysis. By using Predictive Test Selection to only run the tests that truly matter, based on code changes, run times on unit tests, integration tests, end-to-end tests, smoke tests, and regression tests can all be reduced.
Additionally, Predictive Test Selection can improve the accuracy and reliability of test impact analysis, as well as reducing a lot of risk. By running the most important tests for code changes first, devs can make the most needed changes faster, ultimately improving code quality and a release.
An avenue to supercharge your Test Impact Analysis, Predictive Test Selection handles larger codebases and more frequent code changes. As a project grows more complex and complicated, as more and more features are tweaked or added, so too does testing
Launchable’s Predictive Test Selection helps DevOps teams amplify their Test Impact Analysis strategy by ensuring testers only run the most valuable tests, early on, for every change. Create dynamic subsets of the most important tests for code changes in real-time, and slice major time off of testing cycles.
