The Importance of Human Judgment in Software Development

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The idea that so many things in software development are intangible can only really be understood and measured by using human judges. The example we've read in the paper I think is about migrating from like Python 2 to Python 3, right? And that's a very concrete example, but we can imagine ideal states that don't have anything to do with new languages or whatever. But I think that it's that disconnect between what could be and what is. That expert judgment is a really critical thing to technical debt.

This week we’re joined by Ciera Jaspan and Collin Green, who lead the Engineering Productivity Research team at Google. Ciera and Collin have written several papers from studies they’ve conducted, and this discussion covers the insights from their research as well as their work more broadly at Google.

Discussion points:

(1:19) About the Engineering Productivity Research team
(3:57) How the team interacts with the rest of the organization
(5:58) The different backgrounds included on the team
(13:11) How Google measures developer productivity
(18:54) Evaluating discrepancies between qualitative and quantitative data
(28:40) Google’s quarterly developer survey
(32:02) Distributing survey results back to the organization
(40:25) Misunderstandings about surveys
(43:51) Ciera and Collin’s paper on why measuring productivity is difficult
(50:35) Reductionist metrics for measuring productivity
(55:26) Examples of other fields that have struggled with measurement
(59:00) Google’s study on measuring technical debt
(1:08:05) Human judgment in measurement

Mentions and links:
Follow Ciera and Collin on LinkedIn
A Human-Centered Approach to Measuring Developer Productivity - Paper, Abi’s summary
Enabling the Study of Software Development with Cross-Tool Logs - Paper
Defining, Measuring, and Managing Tech Debt - Paper, Abi’s summary
Google’s Goals, Signals, Metrics framework - Paper, Abi’s summary