Sapient.ai today launched a testing automation platform that makes use of artificial intelligence (AI) and automatically creates unit tests as the applications are developed.

Company CEO Rishi Singh said Sapient Codeless makes use of large language models (LLMs) that have been specifically trained to create unit tests and then automatically run them as needed. It analyzes code and then automatically generates declarative tests based on the inputs and outputs of the methods used to build the application.

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Additionally, Sapient Codeless automatically adapts to changes in the codebase, ensuring tests remain up to date in a way that eliminates the need for ongoing test maintenance.

The overall goal is to make it simpler to keep pace with the amount of code that needs to be tested as more organizations make use of AI assistants to write code, noted Singh.

That approach provides the added benefit of significantly reducing the amount of testing code that DevOps teams would otherwise need to manage alongside all the other code they already manage, he added. In some organizations, there is as much as five times more boilerplate testing code that DevOps teams need to manage than there is actual code that ultimately finds its way into an application, said Singh.

As the pace at which applications are developed continues to accelerate, it’s clear existing manual approaches to creating unit tests will not scale. It’s expected many organizations thanks to the rise of AI will build and deploy more applications in the next two years than they might have in the past decade.

However, if most of those applications are not tested more thoroughly before they are deployed most DevOps teams will find themselves trying to debug and troubleshoot more applications than ever long after they have already been deployed in a production environment, noted Singh.

AI-Infused Automation Platforms Make Things Easier

It’s not clear to what degree organizations may need to run more tests versus focusing more on running the right test at the right time. Much time is wasted running tests in a sequence that doesn’t surface a failure until late in the process. In theory, automation platforms infused with AI should make it easier to identify code that is likely to fail a specific test sooner. In effect, DevOps teams should be able to more easily run the right test at the right time.

In the meantime, the debate over who should run what type of test at any given time continues unabated. Many developers are happy to run tests that surface issues as they write code but are not nearly as engaged when tests are run, long after they have moved on to another project. In many cases, they would rather see a DevOps team manage a significant portion of the testing process on their behalf.

Regardless of approach, the one thing that is clear is the overall quality of the applications being deployed needs to improve. The issue now is finding a way to achieve that goal without increasing the overall level of toil already being experienced.