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Engineering Leaders, measure the impact of GitHub Copilot

Understand the impact on developer productivity and satisfaction. Leverage holistic ROI dashboards to communicate the value, monitor the impact, and optimize your rollout.

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Select charts from Faros AI measure AI coding assistant usage by language, quality impact, and PR size impact before and after Copilot.

Some of our customers include

Analytics that go far beyond the basics available from the GitHub Copilot API.

GitHub Copilot API

Faros AI

Developer metrics

Last activity date per developer

Adoption metrics per developer and team (DAU, WAU, MAU)

Copilot usage data

Last 28 days only

Full data history

Impact analysis

GitHub data only

GitHub data correlated with task, deployment, quality, incident, security, and sentiment data from 100+ tools

Team and Power User filters

A/B Testing and Before/After analysis

Out-of-the-box dashboards for tracking adoption, impact, risk and value

Out-of-the-box developer surveys

Personalized AI/ML insights about adoption, usage and impact

Download from GitHub MarketplaceRequest a demo

Comprehensive visibility into the impact of GitHub Copilot on productivity.

Engineering leaders are deploying GitHub Copilot under the watchful eyes of executives who anticipate significant productivity gains. But how do you measure and communicate the impact?

Faros AI illustrates the impact and ROI of AI coding assistants. Track adoption and usage. Measure the benefits. Monitor the risks. Optimize the rollout.

The right implementation can unlock 40% higher ROI

AI pair programming tools are driving up licensing costs, with new tools being announced daily. Improve your return on investment from GitHub Copilot with a full measurement framework that guides you from pilot to rollout to optimization.

Vendor Comparison

WHICH IS THE BEST AI CODING ASSISTANT?

Identify the most effective tool, best-suited to your code and favored by your developers.

Read case study

A/B Testing

IS THE TOOL WORTH IT?

Observe the impact of AI augmentation on different cohorts and profiles.

Before and After

WHAT’S CHANGED?

Measure the time savings. Calculate the economic benefit. Spot shifting bottlenecks.

Shai Peretz

While these tools have the potential to increase productivity, having a way to evaluate their impact scientifically will help build the business case for the investment.

Shai Peretz

SVP Engineering, Riskified

Track adoption and usage over time.

Optimize your rollout for higher GitHub Copilot impact and ROI.

  • Measure daily, weekly, and monthly adoption.
  • Track acceptance rates and lines of code generated, by language and editor.
  • Compare cohorts using different types of licenses (Business vs. Enterprise).
  • See which activities are augmented most: coding, testing, debugging, documenting, etc.
  • Identify unused licenses and understand why.
A Faros AI chart titled Number of Daily Active Users shows active AI coding assistant users growing from 150 to 1000 over a four month period

Measure downstream impacts across the entire SDLC.

Separate hype from reality and set the right expectations for your org.

  • Identify new bottlenecks as adoption increases (e.g., in code review, deployment, QA).
  • Maintain visibility into quality, reliability, and tech debt.
  • Identify potential security and compliance issues in AI-augmented code.
  • Present cost/benefit metrics to executive leadership.
A Faros AI chart titled Lead Time Bottlenecks shows two stacked bar charts side by side. On the left, average duration per stage Before Copilot and on the right, After Copilot. The stages are First Review Time, Review to Approval Time, Merge Time, Time in Deploy, Time in Staging, Time in QA. After Copilot, Review to Approval Time has increased, while Merge Time has decreased.

Understand time savings and developer satisfaction.

Quantify GitHub Copilot productivity gains and analyze changing development patterns.

  • Measure cumulative time savings and understand where they’re being reinvested (velocity, quality, tech debt, upskilling, etc.).
  • Capture developer sentiment with out-of-the-box surveys.
  • Understand which teams and roles are adopting and benefiting most.
  • Improve training and mentoring to optimize your rollout.
A Faros AI chart depicts the Cumulative Time Savings, Equivalent Economic Benefits, and Developer Satisfaction from AI coding assistants like GitHub Copilot.

Frequently Asked Questions

Illustration of a rocket shooting from left to right on dark blue background - Faros AI

Ready to measure the ROI of GitHub Copilot?