Measuring Developer Productivity in Age on GENAI

The GenAI Revolution: Two Years Later

November 30, 2022 marked a pivotal moment when ChatGPT was released, sparking excitement and optimism about increased efficiency across industries. Now, with over two years of GenAI integration, the industry has matured enough to properly evaluate the impact and value of these tools on various aspects of business. In this post, I'll focus specifically on measuring developer productivity.

Measuring Impact: Output vs. Outcome

The impact of any change—whether new tools, processes, or methodologies—can be measured in terms of both output and outcome.

As a product organization, outcomes are ultimately the metrics that deliver revenue or customer growth. However, this same model cannot be directly applied when measuring the impact of GenAI on developer efficiency.

A Framework for Measurement

In this post, I'll share several approaches to measure productivity with GenAI tools, focusing on a progression from:

Output → Outcome → Growth

This framework will help organizations better understand how GenAI affects developer productivity in ways that eventually translate to business value.

Developer productivity can be measured on multiple dimensions

	How Fast (Output)	Is effective  (Output)	Impact (Outcome)	Growth (Outcome)
Primary Metrics	# PR per Engineers # Test Coverage per PR	# Engineering time Index # Non Engineering time index	Failure Rate of Change Usage of Feature	Time spent on new capability/products  Time spent on R&D
Secondary Metrics	Cycle Time for PR Deployment Frequency Perceived rate of productivity	Time on PRs per sprint  Friction in  delivery Code tech Debt Index Code Security Debt Index	Last minute change  Operational & Security Health	ROI on new features  Revenue per Engineers New Products/Segments

 

Finding the Right Mix of Developer Productivity Metrics

The table above outlines four key dimensions for measuring developer productivity in the GenAI era. These dimensions incorporate both quantitative and qualitative metrics, collected through various methods:

Balanced Measurement Approach

Each dimension contains metrics that vary in nature:

• Quantitative metrics provide objective, numerical data that can be tracked over time
• Qualitative metrics capture subjective experiences and insights that numbers alone cannot reveal

Lets start with category of metrics 

How Fast ( Output)

This metric provides a straightforward measure of how effectively development teams leverage generative AI tools to produce code and the rate at which they do so. It serves as an excellent starting point for analysis and can be fully automated for continuous monitoring.

Is Effective ( Output)

This category assesses the quality of output by analyzing the ratio of time spent on engineering versus non-engineering tasks. It also incorporates lagging indicators such as sprint-level pull request review times, code technical debt indices, and security vulnerability indices. These metrics, largely automated, provide insights into both positive outcomes and potential side effects.

Impact ( Outcome)

This category marks the initial phase of measuring the impact of generative AI-assisted work. It focuses on evaluating delivery quality, product usage, and overall product health.

Growth ( Outcome)

This final category focuses on quantifying the tangible value generated by new features, specifically in terms of return on investment (ROI) and revenue. While direct revenue impact may not be immediately apparent in short development cycles, the focus shifts to measuring the time freed up for new capability development and the potential for new product or market segment expansion.

Things to watch while you measure developer productivity. 

Measuring productivity can lead to misleading signals. Organizations should be wary of:

• Spikes in Lines of Code (LOC) that don't mean better output.
• High Commit/PR counts without real progress.
• Long hours, which often signal burnout, not efficiency.
• Burning through story points too fast, which can mean poor planning.
• Focusing only on individual metrics, not team success.
• Using gamification that hurts collaboration.
• Too many unfinished POCs or WIP projects.
• Thinking Generative AI fixes everything
• A pattern of implementing new Generative AI tools at an unsustainable frequency, such as weekly or more

Conclusion

Metrics shared in this post are in between DORA and SPACE and gives holistic view of team productivity gain. 

If you are early in journey then refer to Implementing-genai-in-engineering-teams post that talks about how to implement transformation.