Story points are gameable. Lines of code is a vanity metric. Peer reviews introduce bias. Without data, performance reviews become exercises in recency bias and subjective impression.
Engineering managers face a recurring challenge: How do you objectively evaluate developer performance?
ContributorIQ provides the quantitative foundation for fair, accurate performance evaluations, turning vague impressions into data-backed conversations.
See exactly how each engineer's output has evolved:
Filter by any time period to align with your review cycles: monthly 1:1s, quarterly reviews, or annual evaluations.
Understand when your team does their best work:
Each contributor is classified into a lifecycle stage based on their recent activity:
When a previously "Peak" contributor shifts to "Winding Down," it's time for a retention conversation, before they start interviewing elsewhere.
Know who your experts are across different parts of the codebase:
Performance isn't just about output. It's about sustainable output:
"I think Sarah has been doing good work this quarter. She seems busy in standups and her PRs look solid."
"Sarah made 47 commits this quarter across 6 repositories, with a churn ratio of 0.4 indicating healthy refactoring alongside new development. She's classified as 'Peak' with consistent weekly output. Her DOA scores show she's become the primary expert on the authentication service. While that is great for her growth, it creates bus factor risk we should address through pairing."
ContributorIQ is a tool for informed conversations, not automated judgments. We recommend:
The goal is to replace subjective impressions with objective data, while applying human judgment to interpret that data fairly.
To capture contribution data aligned with your review cycles
Aligned with your review periods
Before 1:1s for context and talking points
As discussion points, not verdicts
Start tracking engineering performance with data.
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