CloudBees Acquires Launchable to Advance Testing Using AI

CloudBees today revealed it has acquired Launchable, a provider of a test automation platform, to enable DevOps teams to improve both application security and software quality. Financial terms of the acquisition are not being disclosed.

Launchable makes extensive use of both machine learning algorithms and generative artificial intelligence (AI) capabilities to streamline testing processes by, for example, providing access to a co-pilot to ensure the right test is run at the right time.
Additionally, if the platform determines a software component is probably going to fail some portion of a test it will alert DevOps teams to that fact rather than wasting time running a series of tests before there is an inevitable failure.

Sacha Labourey, chief strategy officer for CloudBees, said the addition of the Launchable platform provides organizations with testing capabilities augmented by artificial intelligence (AI) that can be easily added to existing DevSecOps workflows.

In general, application testing remains one of the costly bottlenecks with any software development lifecycle (SDLC). AI tools should substantially reduce time spent on unnecessary tests and inefficient workflows because DevOps teams will know which tests matter most.

In addition, they will be able to automatically launch tests whenever a change is made to code as part of a larger DevOps workflow to ensure that tests are not skipped simply because they might take too much time to run. When a test fails, those same AI tools make it easier to surface the root cause of the issue in a way that makes it simpler for developers to address.

Launchable reports that existing customers such as BMW and GoCardless saw a 50% reduction in machine hours, a 90% reduction in test execution times, and a 40% reduction in build times by relying on AI to automate testing workflows.

AI is already accelerating the rate at which code is being written. The challenge now is applying AI to accelerate the pace at which increased amounts of code can move through DevSecOps pipelines. The overall goal is to improve developer productivity by reducing toil that over time increases burnout among both developers and the software engineers that support them, said Labourey.

It may be a while before AI is pervasively employed across every DevOps workflow but organizations that are at the forefront of adoption are seeing productivity gains. It’s not likely AI will replace the need for DevOps teams. After all, the code and suggestions generated by these tools still need to be reviewed by DevOps professionals to prevent the occasional hallucination from being incorporated into a production environment.

However, the one certain thing is thanks to the rise of AI the pace at which applications are being developed and deployed is about to exponentially increase without necessarily having to dramatically increase the size of DevOps teams. The challenge is that if all those applications are not properly tested that increased pace of deployment might just as easily wind up being too much of the proverbial good thing.