The Limitations of Survey Signals on Technical Debt

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Sierra: "The metrics that exist right now within Google and outside of Google just aren't really representing technical debt" The number of to-dos does not accurately represent how people think about technical debt, Sierra says. Number of lint errors doesn't correspond with engineers' perceptions either, she adds.

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