Why are teams struggling with velocity, quality, and cost?
CI/CD improvements are not enough
You have spent the last few years driving CICD improvements, right? If so, you may have reached a similar conclusion as we did that CICD is merely the first step in the improvement journey and not the destination. Test automation takes you further but ironically adds to delivery times because you run tests more often. Thus, something else is the bottleneck in your delivery process.
Where should you focus efforts? What’s your 80/20?
Your delivery pipeline is composed test suites that a commit has to pass to go to production. Every test suite run adds delay to your release. As the application matures, test suites and the number of test suites keep growing in size, further compounding the problem.
Tests are the bottleneck in software delivery
Your delivery pipeline comprises test suites that a commit must pass to go to production. Since tests form the bulk of your delivery process, any impact here radically affects your delivery times. As the application matures, test suites and the number of test suites keep growing, further compounding the problem where every test suite run delays your release.
The Launchable AI-based Test Intelligence Platform
An AI-based Test Intelligence Platform that brings advancements in AI to your development teams. The platform analyzes code and tests metadata to enable developers & team leads to
Improve the dev-test loop by bringing feedback from tests into Slack and provides a test sessions dashboard that brings insights on test failures to developers
Get meaningful insights on tests suite to eliminate tests causing friction
Radically reduce test times while maintaining quality
Code and test metadata are sent from your CI service to the Launchable SaaS. Your code is never sent over to Launchable. Your tests continue executing wherever they reside (on-premises or cloud).
Our secret sauce: machine learning called Predictive Test Selection
Facebook pioneered predictive test selection, a pragmatic risk-based approach to testing. This new approach to Test Impact Analysis uses machine learning to dynamically select which tests to run based on the characteristics of a code change. Historical test results and information about the tested changes are used to train a machine-learning model to achieve this. The model learns the relationships between the characteristics of code changes and which tests passed and failed, enabling a high-quality prediction of which tests to run.
Launchable's Predictive Test Selection product has made the approach turn-key and accessible to every team.
Launchable helps your devs and team leads run an optimized CI/CD pipeline
Insights to fix DevX issues caused by Unhealthy tests
Most developers know there are unhealthy (e.g., flaky) tests in their test suites that cause friction in their development cycle, but they cannot quantify the impact of these tests to their management leaders. Additionally, the picture is not shared across developers—one dev might perceive the impact differently than others. Consequently, insufficient resources are allocated to fixing issues, increasing development friction. Our ML algorithm will find unhealthy tests, such as flaky tests and others. Developers can use this information to fix the problems that impact their developer experience.
Drastically reduce test execution and feedback times with Predictive Test Selection
Predictive Test Selection to testing gives another dimension (test execution times) to testing efforts that can be used to reduce the cost of testing without impacting either quality or speed of delivery.
It can be used in a variety of use cases, such as:
It applies to verticals including:
Auto-triaging to help find issues that need to be investigated
With Personal Notifications via Slack & Test results and reports, Launchable provides a richer view of test results, helping developers triage and fix failures quickly. Personal notifications imply that a developer gets notified of test status that impacts them. When paired with Predictive Test Selection, the iterative loop for the dev-test becomes lightning-fast.