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
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?
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?
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