Proof of Value

Diffblue’s Proof of Value (PoV) program aims to enable technical evaluation of Diffblue Cover in the context of the expected business value.

It is designed to be an in depth opportunity to learn about our autonomous AI-powered unit testing solution and to help customers validate their technical architecture and business processes in order to be able to effectively evaluate Diffblue Cover.

The solution, its practical application, and how it drives specific business value according to the specific use case you have in mind (examples below) are proven and documented throughout the PoV process.

Advantages of working with the Diffblue PoV program

Our Proof of Value (PoV) exists to achieve the following.

• Help potential customers get the most out of Diffblue Cover in the shortest amount of time possible whilst understanding how it is like to work with Diffblue as a partner.

• Demonstrate the business value and tangible benefits of Diffblue Cover.

• Quantify efficiency gains, cost savings & other benefits that the solution brings.

The Diffblue PoV Program provides several advantages for potential customers looking to evaluate Diffblue Cover including the following.

• Direct access to Diffblue resources, tools and expertise to help customers accelerate their deployment and adoption.

• Ensures that potential customers are successful by providing guidance and best practices from Diffblue Solutions Architects.

• Builds a strong business case, by quantifying specific product benefits and aligning them with strategic objectives.

This guide is specifically intended as an overview of our PoV process and to help set expectations.

Goal

With the Difflblue Cover Proof of Value program, we aim to identify your best deployment path to leveraging autonomous AI-powered unit test generation for your team/engineering organization and to demonstrate product value.

The goal is not to implement a complete product installation & process workflows. Instead, it’s intended to test and validate assumptions related to process, the core AI-driven technology and to demonstrate that the core feature capabilities are valuable.

Specifically, the goal is to prove that Diffblue Cover:

• writes high quality unit tests at scale,

• writes understandable human-like unit tests that compile,

• effectively prevents regressions, and

• significantly saves developer time/resources.