A reading list on developer productivity metrics
Measuring the productivity of software developers is a complex and often contentious topic. Simplistic metrics like story points or lines of code are widely criticised for creating the wrong incentives and failing to capture the true nature of engineering work. The focus is shifting towards measuring project outcomes and team health rather than individual output.
Laura Tacho has curated a comprehensive reading list that provides an excellent overview of the modern landscape of developer productivity metrics. It serves as a valuable resource for anyone looking to move beyond flawed traditional measures.
The list is organised into several key sections, providing a structured path through the subject.
Frameworks overview
This section covers the foundational frameworks used to think about and measure productivity and developer experience today. It includes:
- The AI Measurement Framework: For tracking AI adoption and impact.
- DX Core 4: A unified framework combining DORA, SPACE, and DevEx.
- SPACE Framework: The original paper outlining Satisfaction, Performance, Activity, Communication, and Efficiency.
- DORA Metrics: The four key metrics for software delivery performance.
- DevEx Framework: Focuses on feedback loops, flow state, and cognitive load.
Measuring the impact of AI
With the rise of generative AI, measuring its actual effect on productivity is crucial. This part of the list offers guides on how to measure GenAI adoption and its impact on developer experience and throughput, cautioning that its use is not a guaranteed productivity boost.
Key articles and perspectives
The collection includes several important articles that provide context and critical perspectives. It references the debate around the McKinsey article on productivity, offering a counterpoint from The Pragmatic Engineer. It also highlights foundational pieces from Martin Fowler and Bryan Finster on effectiveness and the potential misuse of DORA metrics.
Laura includes her own strong perspective on the human element of measurement:
One thing I won’t shut up about is that developers are adults, and if they tell you something is preventing them from working efficiently, you should believe them.
This list is a valuable addition, providing extra detail to my earlier posts on the various frameworks for measuring developer productivity and a deeper look at AI’s impact through the SPACE framework. It is a nice, comprehensive overview that brings together many of the current standards and debates in one place.
The full, detailed reading list is available here:
Developer Productivity Metrics - Reading List (Google Docs)
