Measuring Impact: The GenAI Code Report

This LinearB Research Report breaks down how to measure the impact of Generative AI code across the software delivery lifecycle. Inside you’ll find:

12+ metrics to track the impact of your GenAI Initiative

How to measure the adoption, benefits and risks of GenAI Code

Key insights from our survey conducted on 150+ CTOs, VPEs and Engineering Managers

What's Inside?

GenAI Adoption, Benefits, and Risks

As with any new tech rollout, the next question quickly becomes: How do we measure the adoption, benefits, and risks of this investment? The GenAI Code Report breaks down how to use metrics to answer questions like:

Are we getting the intended benefits from GenAI?

Are GenAI PRs taking longer for my team to review?

Which parts of our codebase are being written by a machine? New code? Tests?

Survey Results

We polled 150+ CTOs, VPs of Engineering, and Engineering Managers – hailing from start-ups and Fortune 500 companies alike. Inside, you’ll find their answers to the following questions:

What are your most likely use cases for a GenAI coding tool?

What are your main concerns about using a GenAI coding tool?

How likely is your organization to invest in a GenAI coding tool in 2024?

AI Metrics

Inside, we define the most important AI impact metrics broken down by adoption, benefits, and risks – and how you can start measuring them today. Learn how to build a custom dashboard for essential metrics like:

PRs Opened, PRs Merged

Merge Frequency, Coding Time, Planning Accuracy

PR Size, PRs Merged Without Review, Time to Approve, and more

Read the Docs

Measuring Impact: GenAI Code Workshop

In this workshop, LinearB CTO Yishai Beeri and Thoughtworks Global Lead of AI Software Delivery Birgitta Boeckeler cover:

Data insights from our GenAI Impact Report

Case studies into how others are already doing it

Impact Measures: Adoption, benefits & risk metrics

Live demo: How you can measure the impact of your GenAI initiative today