How to Measure Engineer Productivity at Different Levels (Filed under DevEx)

How to Measure Engineer Productivity at Different Levels (Rubric #DevEx)

There's a tape. speech Stanislav Moiseyev, director of the T-Bank R&D Center with Big Tech Night. In this report, Stas reviews the best practices of recent years and links them to the wave of GenAI tools. Za. 5 Over the years, the landscape has changed a lot: covid → remote → the introduction of AI. Companies are investing in AI development tools, but without measurement, it’s hard to prove ROI. The report shows which metrics answer the question “what really accelerates the delivery of value” and mentions the methodology of the industrial survey. 2025 To assess the impact of GenAI on development.

When it comes to levels, they are like IC. (individual contributors) -> commands> companies> whole industry. And it works like this.

**- IC (individual engineer)**It is not necessary to follow the “line code”, but the flow time. (How many hours without switching?), cycle time of their PR, share of reworks / rollbacks, quality review. It helps to see how the tools (IDE plugins, assistants, autotests) It affects your personal speed and accuracy. A meaningful set of metrics is well described by the SPACE framework. (Satisfaction, performance, activity, communication, efficiency). - Team.: classic DORA set (Frequency of Deployments, Lead Time for Changes, Share of Failed Changes, MTTR) Operating flow metrics: WIP, PR size, proportion of "expectation" vs. "work", sprint stability. These indicators correlate with predictability of delivery and quality. - Company: Here, end-to-end metrics are important: cost-per-change, time-to-value, onboarding speed, autotest coverage of critical paths, utilization of paid AI-power for the development of vs. “toys”, saving engineering hours from assistants. The goal is to link engineering metrics to P&L: how much the speed gain cost and how it impacted the business. - Industry.: benchmarks and comparative surveys. The report outlines an approach to industrialisation study 2025 year by the influence of GenAI – so that companies can relate their practices to the “average temperature on the shop floor” and separate hype from the effect (I already am. told about this research before)

As for what to look for when using metrics, Do not use anti-metrics: “line code”, “number of tasks”, “bug closing speed” without context. They easily play games and push for bad decisions. Do not use metrics without feedback: any graph should lead to a specific solution to a process or tool. Do Not Measure Your Hospital Average Temperature: Compare Yourself to Your History (trend)Not a neighboring department with a different context or architecture.

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