Diffblue Integrates Generative AI-Based Testing Platform With GitLab

Diffblue this week generally made available an integration between its automated unit testing platform for Java and the DevSecOps platform from GitLab.

The Diffblue Cover platform makes use of generative artificial intelligence (AI) to automatically create unit tests based on reinforcement learning technologies that don’t rely on LLMs—avoiding their drawbacks of sometimes introducing hallucinations and also requiring human review.

Diffblue CEO Mathew Lodge said the integration with the continuous integration capabilities found in the premium and ultimate editions of the GitLab platform would, for example, streamline regression testing in a way that should ultimately improve both application quality and security. Diffblue Cover, for example, can update tests as much as 250 times faster than a human developer can write them manually without developer review.

That approach also serves to reduce the level of friction many DevSecOps teams encounter when bottlenecks involving testing processes emerge, noted Lodge. The overall goal is to make it simpler for developers to test as much of their own code as possible before integrating it into a build, he added. Otherwise, developers will get fed up because testing is continuously breaking the build, noted Lodge.

Ultimately, instead of having to write unit tests, developers become supervisors of a platform that automatically generates them on their behalf, said Lodge. The job of a developer doesn’t go away, but it does fundamentally change, he added.

To achieve that goal, developers need to be able to access a platform that writes the tests and can then also execute them automatically. If it takes too long to create the test, chances are high that most developers won’t run it. On average, writing and evaluating a single unit test can take a developer 10 minutes. Over the course of any project, thousands of tests need to be written, so the amount of time testing takes away from coding is often much greater than most IT leaders fully appreciate.

Automating those tests should improve developer productivity as more time is available to focus on writing code rather than testing. That doesn’t necessarily eliminate the need for a dedicated testing team, but it does mean that more tests will be run without slowing down the overall pace of application development.

Developers naturally want to be able to test code at the very instant they create it. AI platforms can make that happen by, for example, employing reinforcement learning to write unit regression tests.

Most developers are not going to resist assuming more responsibility for testing if the tools to automate that task are more accessible. Instead of merely shifting responsibility for testing left toward developers, DevOps teams need to find ways to streamline the process. Otherwise, testing just becomes one in a series of tasks that are being shifted to developers in ways that many of them are coming to resent.

It may take some time before AI is fully integrated into software engineering but it’s clear with each passing day more previously manual tasks are being automated. Among the lowest hanging fruit for applying AI to software engineering are clearly testing processes that, if truth be told, few indeed enjoy conducting.