One company participating in our research was shown to have its best teams in Poland, Brazil, and Romania.
The teams in these countries outperform the global average by ~50% as measured by our novel algorithm developed from research at Stanford University.
Due to having acquired other companies, this company operates large, independent teams in various locations around the world.
This case study stands out because the teams in each country operate as an independent unit, use similar tech stacks (JS, Python), and develop products at comparable stages of business and technology maturity.
Team-level productivity measurement is particularly insightful: it encourages people to “grow the pie” (increase total team productivity) rather than “slice the pie” (focus on individual productivity and compete for existing resources).
When making decisions about your software teams, it’s important to use objective metrics within the right context.
Be cautious with traditional productivity metrics (e.g. Lines of Code, # of Commits, DORA Metrics), as they may not provide an accurate picture.
Traditional developer productivity metrics don't analyze the source code written, don’t reliably measure developer productivity, are often easily manipulated, and can incentivize undesirable behavior.
Methodology:
-Using an algorithm developed from research conducted at Stanford University, we quantitatively assess developer productivity by evaluating the impact of commits on the software's functionality (ie. we measure output delivered).
-The algorithm generates a language-agnostic metric for evaluating & benchmarking individual developers, teams, and entire organizations.
About Our Mission:
-This is part of a research initiative at Stanford University focused on quantifying software engineering productivity.
-Our goal is to help engineering teams make decisions based on transparent and objective data, not intuition and politics.
-Our research participants use our algorithm to make decisions about work methods, team setup, outsourcing, tool use, etc.
