How Launchable works

AI-powered test automation

Today, many software projects have long-running test suites that run all the tests each time they are run, in no particular order. When you are working on a small change in a large project, this is wasteful. You know that only a few tests are relevant, yet there’s no easy way to know exactly which tests to run.

Launchable uses AI to generate test recommendations base on code changes

The core of the Launchable platform is a machine learning algorithm that predicts the likelihood of failure for each test based on past runs and the source code changes under test.

This lets you reorder your test suite so that tests that are likely to fail are run first. You can also choose to run dynamically subset of tests that are likely to fail, reducing a long-running test suite to a few minutes.

How the machine learning model is trained

Every time tests run, the results are passed to Launchable to continuously train the model.

Model training looks at the changes associated with each build and which tests failed when tests ran. It builds associations between line changes in 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.

Model training generally takes between 3 and 10 weeks, depending on how often tests run and how many tests fail in a typical run. More data improves the quality of the model, but reasonable accuracy is generally obtained within a few weeks.

Calculating test order

One way to think about how Launchable reorders your tests is that with each successful test, Launchable's confidence grows that the entire run will be successful. The ideal algorithm optimizes for the highest confidence as early as possible.

So confidence and individual test run time are the two primary determining factors for test reordering.

Confidence is a function of the probability of failure for each individual test as tests run. Tests with a high probability of failure yield a higher confidence gain when successful. When tests with a low probability of failure pass, they yield smaller confidence gains.

Launchable reorders test suites so that the tests with a high likelyhood of failure are run first

Since our goal is to deliver as much confidence as quickly as possible, it makes sense for Launchable to deprioritize a long-running test if the confidence gain from that single test is not high enough to offset the gain of running shorter tests during the same period of time. This is exactly what the Launchable algorithm does.

For example, if test T8 has a high probability of failure and takes 3 minutes to run and test T4 has a slightly lower probability of failure but only takes 300 milliseconds, Launchable will prioritize the shorter test (T4) before the longer test (T8) because it yields a higher confidence gain in a shorter period of time.

We are currently exploring other factors that may influence test order such as cases where tests have order dependency on other tests. Contact us if you're interested in learning more.

Subsetting tests

If your tests take a very long time to run, you should consider running a subset of your tests earlier in your development cycle. We call this use case "shift-left". For example, if a test suite that runs after every merge takes 5 hours to run, a 30-minute version of the same suite could be run on every git push while the pull request is still open.

While you could accomplish this by manually selecting tests to run, this has the disadvantage that the tests most relevant to the changes present in a build may not be run until much later in the development cycle. Launchable provides the ability to create a subset based on the changes present in the build every time you run tests. We call this a dynamic subset because the subset adapts to your changes in real-time.

Launchable allows you to dynamically subset your test suite based on code changes so that the only the tests that are likely to fail are run

A dynamic subset leverages the same test sequence generated for reordering tests but only returns the first part of the total sequence to your test runner. The cutoff point can be based on either the maximum length of time you specify (30 minutes in the above example) or the minimum confidence level you wish to achieve.

Launchable also provides the ability to run the rest of the tests that were not selected in your dynamic subset in a separate run.

How it integrates with your test suite

Launchable can be quickly integrated with your test suite through your existing pipeline script and the Launchable CLI:

Basically, when a build is ready for testing:

  1. Your CI tool sends the changes in the build, the full list of tests to run, and a target for the test run (e.g. 20 minutes) to Launchable.
  2. Launchable responds with which tests to run based on the changes in the build and the target.
  3. Your CI tool runs the subset of tests using your build tool or test runner.
  4. When the run ends, your CI tool sends the test results to Launchable to continuously train the machine learning model.

What data is sent to our servers?

Launchable’s machine-learning algorithm learns the relationship between code changes and the tests impacted by those changes through metadata that is sent to the Launchable API through your test runner.

We do not currently employ static code analysis, so the full contents of your source code does not need to be sent to our servers. The data that is sent currently includes...

Metadata about the code changes being tested:

  • the names and paths of files added/removed/modified in the change
  • number of modified lines in files in the change
  • the location of modified lines in the files in the change
  • Git commit hashes associated with the change
  • Git author details associated with those commits

Metadata about the test cases that were run:

  • the names and paths of test cases and test files
  • pass/fail/skipped status of each test case
  • the duration (run time) of each test case
  • test case associations to test suites (e.g. ‘unit tests,' ‘integration tests,’ etc.)

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