Save time and improve testing quality by making your pipeline more data-driven with advanced test impact analysis
Test Impact Analysis is a technique for identifying the most critical tests to run for a specific code change. Static code analysis is traditionally used to determine which tests are most likely to be affected by changes to a code base.
Launchable uses a variant of Test Impact Analysis called Predictive Test Selection to allow users to create real-time subsets of the most important tests for code changes. Run only a few minutes of each test suite with each git push, rather than waiting for hours to receive feedback on their changes.
What is Test Impact Analysis?
Test Impact Analysis is a method of evaluating the potential impact of a code change on the overall testing process. It is used to determine which tests are the most important to run after a code change has been made, in order to ensure that the change has not introduced any regressions or defects into the system.
Test Impact Analysis can be used to optimize the testing process by identifying and focusing on the tests that are most likely to be affected by the code change, rather than running all tests every time a change is made. This can save time and resources, and improve the efficiency of the testing process.
Standard Static Code Analysis
Static code analysis is a method of evaluating the quality of source code by analyzing it without executing it. It is typically used to identify potential issues or problems in the code, such as bugs, security vulnerabilities, style violations, and performance issues. Static code analysis can be performed manually, or by using automated tools that scan the code and report any issues that are found.
It is often used during the development process to catch problems early on, and can also be used as part of a code review process to ensure that the code meets certain standards or guidelines. Static code analysis can help improve the quality and reliability of a software system, and can also be used to enforce coding standards and best practices.
Traditional Test Impact Analysis Benefits and Limitations
When done efficiently, test impact analysis can cut the amount of time spent re-testing and improve developer experience. But whether your project is a small microservice or a giant monolith, the amount of test data you need to work with can get large quickly.
Improved efficiency
Test Impact Analysis can identify the tests that are most likely to be affected by a code change, which allows developers to focus their testing efforts on those tests. This can save time and resources, and improve the efficiency of the testing process.
Faster feedback
By running only the most important tests after a code change, developers can receive feedback on their changes more quickly. This can help them identify and fix issues early on in the development process.
Reduced risk
By ensuring that the most important tests are run after a code change, Test Impact Analysis can help reduce the risk of introducing regressions or defects into the system.
Improved code quality
By focusing on the tests that are most likely to identify issues in the code, Test Impact Analysis can help improve the overall quality of the codebase.
Increased confidence
With more thorough testing coverage, Test Impact Analysis can help increase the confidence of developers in the stability and reliability of their code.
Minimize Test Run Time by Scaling Test Impact Analysis with Predictive Test Selection
Launchable’s Predictive Test Selection solves traditional Test Impact Analysis limitations by harnessing machine learning to streamline intelligent test selection for faster, scalable testing. Using this data-driven approach, Predictive Test Selection determines which tests best suit each type of code change.
Get faster feedback earlier
Harness advanced test impact analysis for faster test insights.
Integrate with existing CI
Seamless integration with your CI, regardless of commit frequency or the number of Git branches you have.
Scale with any pipeline
Supports all apps and languages, and teams report up to a 90% reduction in test times without risking quality.