Static smoke tests may miss critical issues

As test suites grow, teams put together a static list of tests (“Smoke”) considered a representative of the codebase. The idea is that these would be run on some cadence to flag issues earlier. The problem with the approach is that these tests don’t factor incoming commits, and teams might be testing areas that aren’t impacted, overlooking the ones that are.

Manually curated smoke tests may miss critical issues

Solution: generate dynamic subsets factoring incoming commits

Facebook pioneered the predictive test selection approach, and Launchable's Predictive Test Selection product has made the approach turn-key and accessible to every team. Launchable’s technology uses ML to predict which tests will likely fail in your test suite based on the incoming commits. This pragmatic risk-based approach to testing can create dynamic subsets tailored to quickly finding failures.

AI-curated tests tailored to find failures quickly

Benefit: dynamic subsets can find pertinent issues faster

You run the tests that are likely to fail and box them as dynamically generated subsets. The size of the dynamic smoke tests can be changed on the fly. If you are looking for fast feedback, optimize for a smaller box. If you are conservative, you ask the AI to select a higher confidence threshold.

Launchable AI provides a dial in the hands of developers to make the appropriate tradeoff.

Teams can take this one step further and look ahead further in the pipeline across various test suites to Use case—Shift-left appropriate tests. Thus, you can have a series of dynamic smoke tests that optimize each test suite in the delivery pipeline.


What do you mean by “In-place” reduction of tests?

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.

What do you mean by a pragmatic risk-based approach to testing?

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?

What about slippage or tests that you don’t catch?

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.

How does this work with code coverage?

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.

You are asking me to think differently about testing. Is anybody else doing this?

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.

The Launchable Test Intelligence Platform also offers Test Insights to find inefficient test suites and Test Notifications to speed up feedback to developers.

Works with your existing tools, languages, and processes

Results in weeks—no months-long DevOps transformations

Launchable's ML-based approach means it can work with existing languages and tools. Developers start seeing their dev cycles go faster without changing their processes.