The key challenge development teams: CI/CD is not enough to reduce delivery times
Tests, though necessary, are the biggest bottleneck in your delivery pipeline
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.
Solution: predict which tests will fail, and exclusively run those tests
Facebook pioneered an approach called predictive test selection and Launchable’s Predictive Test Selection product has made this approach turn-key, and accessible to every team. Launchable’s technology uses ML to predict which tests are likely to fail in your test suite, based on the commits that are coming in.
This is a pragmatic risk-based approach to testing. You simply run those tests that are likely to fail and see massive drops in test execution times (up to 90%).
Benefit: radically shortened test suite runtime, and faster delivery pipelines
A test suite can now be run in a fraction of the time (up to 80% faster) to find failing issues more quickly. These teams run an entire defensive run later in the pipeline to mitigate the risk of failure landing in production. The approach can be used on all test suites in a delivery pipeline to reduce application delivery time.
Simply, we ask you to reduce the execution of a particular test suite wherever it is currently running (“in-place”) in the pipeline. This, as opposed to shifting a test suite left in the pipeline.
Not every test is likely to fail on a commit but we still run them because we don’t know which test would likely fail. With the advancements in AI/ML, we can now predict which tests are likely to fail with a reasonable degree of confidence. If teams are okay with the reasonable degree of confidence, you can radically reduce execution times. How significant? 40-80% is typical and sometimes even more. Sai was able to reduce 90% of execution time at 90% confidence for 75 developers. We give a dial to the teams to make a data driven decision on the trade-off between confidence and execution time.
In our opinion, testing needs to evolve to catch up to the incredible advancements in AI. The technology is ready and here now, are you?
Great question! What we are not saying is to ship bugs to production!
The answer is “it depends on your degree of risk”. For example, Sai (in the earlier use-case) was able to bring 90% reduction with 90% confidence. IOW, he was okay in letting 1/10 test failures pass because he is relatively early in the pipeline. He can do so because he has tests later in the pipeline that catch slippages.
Typically, customers do one or more of the following:
Have a defensive test run sometime later in the pipeline. This can show up as the same test suite has a “full run” later in the pipeline.
Depend on later test suites to catch with the issue. Thus, their approach is to bring this in earlier in the pipeline rather than later.
Couple this with practices like feature flags (applies to SaaS companies) such that they can rollback potential issues.
Be fairly conservative on the time/confidence ratio in the adoption phase of Launchable. For example, it is not uncommon for companies to start with something like 20-30% reduction in test times and slowly ramp that up over time.
Take the release phase into consideration in making the tradeoff. For example, for packaged software, it might be easy to be very aggressive early on and dial the tradeoff down as release approaches.
Indeed, we are asking you to rethink testing and upgrade it in the new world of AI. Facebook and other leading companies are doing this in-house but it requires an in-house team of ML experts. A number of companies are using Launchable’s turn-key approach.
This is a complementary approach with a caveat. The caveat is that you no longer run code coverage report on every test suite run. Code coverage reports are run at a cadence that depends on what test suites are being run and what makes sense for your organization.
For example, if you optimized nightly integration test runs, you may run the code coverage report in the end of the week run.