Best Practices to Measure the Benefits of GitHub Copilot

Key Content Summary

Measuring the impact of GitHub Copilot requires tracking downstream velocity and quality KPIs, identifying bottlenecks, and leveraging causal analysis.
Best practices include focusing on teams with high Copilot adoption, monitoring PR cycle time, lead time, throughput, and quality metrics such as CFR and MTTR.
Faros AI provides actionable intelligence and automation to operationalize these measurements and drive business outcomes.

Why Trust Faros AI on Developer Productivity Measurement?

Faros AI is a leading software engineering intelligence platform trusted by global enterprises to optimize developer productivity, engineering efficiency, and DevOps maturity.
Faros AI delivers measurable performance improvements, such as a 50% reduction in lead time and a 5% increase in efficiency, handling thousands of engineers and hundreds of thousands of builds monthly without performance degradation.
Faros AI's platform is designed for large-scale, complex organizations, providing unified visibility, AI-driven insights, and automation across the software development lifecycle.

How Faros AI Helps Address Engineering Pain Points

Engineering Productivity: Faros AI identifies bottlenecks and inefficiencies, enabling faster and more predictable delivery. Customers have achieved significant improvements in throughput and team health.
Software Quality: Faros AI tracks quality KPIs (bugs, incidents, CFR, MTTR) and ensures reliability, especially for code from contractors.
AI Transformation: Faros AI measures the impact of AI tools like Copilot, runs A/B tests, and tracks adoption, enabling organizations to maximize ROI.
Business Impact: Customers report 50% reduction in lead time, 5% increase in delivery efficiency, and enhanced reliability and visibility into engineering operations.
Case Studies: Organizations such as Autodesk, Coursera, and Vimeo have achieved measurable improvements using Faros AI (Customer Stories).

Faros AI Platform Features and Benefits for Enterprises

Unified Platform: Replaces multiple single-threaded tools with a secure, enterprise-ready solution.
AI-Driven Insights: Provides actionable intelligence, benchmarks, and best practices for engineering teams.
Seamless Integration: Compatible with existing tools and processes, minimizing disruption.
Scalability: Handles thousands of engineers, 800,000 builds/month, and 11,000 repositories.
Security & Compliance: SOC 2, ISO 27001, GDPR, and CSA STAR certified; features audit logging and data security.
Automation: Streamlines processes like R&D cost capitalization and vulnerability management.
Support & Training: Email & Support Portal, Community Slack, Dedicated Slack Channel for enterprise customers.

Best Practices for Measuring GitHub Copilot Benefits

Measure Downstream Velocity KPIs: Track PR Cycle Time, Lead Time, and Task Throughput. Focus on teams with >50% Copilot adoption for meaningful results.
Measure Downstream Quality KPIs: Monitor bugs, incidents, CFR, MTTR, and vulnerabilities. Adhere to coding standards and use Faros AI to correlate quality metrics with Copilot usage.
Identify Shifting Bottlenecks: Visualize cycle times and compare metrics before and after Copilot adoption to pinpoint constraints.
Leverage Causal Analysis: Use Faros AI's machine learning capabilities to determine if productivity gains are attributable to Copilot.

Frequently Asked Questions

How does Faros AI measure the benefits of GitHub Copilot?

Faros AI tracks adoption, usage, time savings, economic benefit, and developer sentiment. It correlates these metrics with downstream velocity and quality KPIs to provide a holistic view of Copilot's impact.

What business impact can customers expect from Faros AI?

Customers can expect a 50% reduction in lead time, 5% increase in efficiency, enhanced reliability, and improved visibility into engineering operations.

What support and training does Faros AI offer?

Faros AI provides onboarding guidance, technical support via Email & Support Portal, Community Slack, and Dedicated Slack Channel for enterprise customers.

How does Faros AI ensure security and compliance?

Faros AI is SOC 2, ISO 27001, GDPR, and CSA STAR certified, with robust audit logging and data security features.

Related Resources

See Faros AI in Action

Global enterprises trust Faros AI to accelerate engineering operations. Request a demo and see measurable results for yourself.

How to measure the benefits of GitHub Copilot — best practices

After three to six months with GitHub Copilot up and running, leadership will be knocking at your door to answer their big question: “How has the world’s most famous AI coding assistant increased our developer productivity?” To answer that question, you need data to illustrate the benefits of GitHub Copilot on engineering outcomes.

When framed within the Launch-Learn-Run framework, you’ve reached the Run phase.

During the initial Launch phase, you focused on understanding organic adoption and usage.

In the subsequent Learn phase, you gathered insights from developer surveys, ran A/B tests, and analyzed before-and-after metrics for early adopters and power users.

Now, in the Run phase, you need to measure downstream impacts across the SDLC to ensure individual benefits of GitHub Copilot have resulted in collective productivity gains.

ROI metrics encompass more than just time savings and developer satisfaction; they must also reflect the primary business goals of delivering features to customers faster, maintaining high quality and reliability, and supporting business growth.

With so many of your developers using GitHub Copilot, you’ll be able to measure whether its adoption is moving the needle on collective KPIs—the lagging indicators to better and faster coding.

In the final article of our series, we explore best practices for measuring and communicating the full benefits of GitHub Copilot.

Measure downstream velocity KPIs

As your rollout expands and adoption grows, individual developer's time savings and productivity gains should ultimately translate to faster end-to-end delivery and improved collective outcomes.

GitHub Copilot likely generated time savings for your developers, increasing their personal velocity as measured by PR Merge Rate. Once the code is merged, dependencies kick in—on reviewers, QA, and deployment processes.

To measure GitHub Copilot’s downstream benefits from faster coding, measure the following velocity metrics:

PR Cycle Time: Is the whole cycle getting faster or are the gains being erased?
Task Cycle Time (or Lead Time, depending on your taxonomy and processes): Are tasks completing faster end to end?
Task Throughput: Are developers completing more tasks?

Best practice: Look closely at teams where GitHub Copilot usage is high. Teams with low adoption will not have measurable downstream impacts. Leverage your usage data to compare and contrast KPIs for teams (or teams of teams) that have achieved over 50% adoption. Look at their metrics before and after the 50% usage threshold has been crossed.

bar graph depicting PR cycle time above 50% Copilot usage

Measure downstream quality KPIs

After several months of adoption, downstream impacts on quality may come to bear. Proactively monitoring changes to quality KPIs will help put the right guardrails in place. Similarly, if your metrics show quality is holding steady or even improving, your confidence to expand GitHub Copilot licenses will increase.

Gather metrics from quality, support, and incident management tools to observe the impact on metrics like:

Bugs per developer
Incidents per developer
Change Failure Rate (CFR)
Mean Time to Recovery (MTTR)
Vulnerabilities

Best practice: Continue evaluating and enhancing the quality, reliability, and security of AI-generated code. Define and adhere to business-approved coding standards to prevent avoidable future issues.

Identify shifting bottlenecks

Every organization and sub-organization is going to experience different immediate gains and downstream impacts depending on their context and DevOps maturity. For example, a team with a feature-flag-controlled fully continuous deployment process and extensive test automation may see the gains in faster coding times directly translate to faster end-to-end lead times and more frequent deployments. Other teams may have more work to do to get there.

Best practice: Monitor shifting bottlenecks. Visualize the cycle times within a metrics to understand where the work is slowing down. Comparing cycle tiems before and after Copilot adoption helps identify the constraints you need to tackle to capitalize on accelerated coding.

bar graph depicting lead time bottlenecks before and after Copilot

Leverage causal analysis if gains don’t materialize

At any given moment, multiple factors that influence developer productivity are at play. So if there is no positive improvement in your metrics—how can you be sure it’s related to Copilot? Tools like Faros AI utilize machine learning to conduct causal analysis of these metrics, and can answer this question. To find out more, contact us.

Maximizing the long-term benefits of GitHub Copilot

Once you’ve measured downstream impact, you’ll be able to have meaningful, data-driven conversations with leadership that justify the tool’s continued use and expansion. Following the best practices in this series will set your engineering organization up to experience maximum benefits of GitHub Copilot.

Best Practices to Measure the Benefits of GitHub Copilot

Key Content Summary

Why Trust Faros AI on Developer Productivity Measurement?

How Faros AI Helps Address Engineering Pain Points

Faros AI Platform Features and Benefits for Enterprises

Best Practices for Measuring GitHub Copilot Benefits

Frequently Asked Questions

How does Faros AI measure the benefits of GitHub Copilot?

What business impact can customers expect from Faros AI?

What support and training does Faros AI offer?

How does Faros AI ensure security and compliance?

Related Resources

See Faros AI in Action

Thank you!

Thank you!

How to Measure the Benefits of GitHub Copilot — Best Practices

How to measure the benefits of GitHub Copilot — best practices

Measure downstream velocity KPIs

Measure downstream quality KPIs

Identify shifting bottlenecks

Leverage causal analysis if gains don’t materialize

Maximizing the long-term benefits of GitHub Copilot

Additional blogs in this series:

Neely Dunlap

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