Glossary
Chat interface for managers: “Show me top blockers this week”
LLM analyzes PRs, comments & patterns to suggest improvements
Work contributing to quarterly OKRs or strategic initiatives
% of engineers leaving voluntarily per year (target: <10%)
Functional defect in production or pre-release code
Scheduled meetings reducing available focus time
% of production deploys that cause incidents or rollbacks
PRs merged per week, normalized by team size
Levels 1–5 in pipeline design, testing, security, observability
Specific, metric-driven recommendations for managers to coach effectively
Expected review depth, response time, cross-team PRs
Feedback left on PR diff, ticket thread or design doc
Regular, data-informed 1:1s & async feedback loops to improve performance
Small, frequent process tweaks backed by data
Team norm: retros, experiments, kaizen events
Coding, system design, testing, collaboration, ownership
Post-release survey: “How satisfied are you?” (1–5)
From task start (e.g. “in progress”) to done (merged/deployed)
15-min team sync: what I did, will do, blockers
Conclusions drawn from unified Git, Jira, CI/CD & calendar data
Decisions based on metrics, A/B tests, not opinions
Duration from merge to production (CI/CD pipeline runtime)
Uninterrupted deep work blocks (ideal: 4+ hrs/day)
Composite score: output × quality × velocity × sustainability
Hours from “in dev” to “ready for review”
% time on features, bugs, tech debt, support, learning
Given data, budget & authority to fix team issues fast
“How likely to recommend this team?” (-100 to +100)
End-to-end duration from idea to value in production
Number of urgent post-release fixes per sprint (target: <1)
Total time from detection to permanent fix deployed
Minutes from alert to first engineer action
“Keeping The Lights On” – operational, support & maintenance work
GPT-like models power summaries, risk detection & natural language queries
Time from first commit to production deployment (DORA metric)
Auto-refreshing UIs showing current team state
Real-time Kanban of all in-flight PRs and tickets
% of time on strategy, coaching vs firefighting & admin
Number of meetings + Slack pings per day disrupting flow
Avg uptime between production-breaking incidents
Avg duration to restore service after production incident
Hours from final approval to actual merge into main branch
User-facing functionality delivering product or business value
Standard checklist + metrics for new hire integration
Weekly 30-min manager-engineer sync on goals, blockers, growth
Stage 1–4: chaotic → reactive → standardized → optimized
AI-powered platform unifying dev metrics, AI insights & management tools for engineering leaders
30–90 day plan with metrics to address performance gaps
Ongoing cycle: set goals → track → review → improve
Systematic, fair tracking of individual & team contributions over time
Quarterly/annual formal assessment of impact & growth
Anonymized data, role-based access, no PII in analytics by default
AI flags at-risk engineers or processes before issues escalate
Proposed code change submitted via Git for review and merge
Peer code inspection with comments, approval or request changes
Total lines added + deleted; small = <200, large = >1000
% of engineering time on innovation vs maintenance
Live alerts on stalled PRs, high WIP, low focus time or rising tech debt
Average deploys per week (elite teams: multiple per day)
Self-service dashboards for devs, PMs, designers
% of devs still employed after 12 months (target: >90%)
Average rounds of feedback per PR before approval
Average comments per 100 lines changed in PR
Number of open PRs awaiting review per active reviewer
% of team members who reviewed at least one PR this week
Median hours from first review to final merge (excl. author fixes)
Post-merge fixes due to bugs, review misses or scope changes
Contractual commitment to customers (e.g. 99.95% availability)
Internal reliability target (e.g. 99.9% uptime)
Statistical models on velocity, quality, burnout risk
Real-time, customizable views of team velocity, quality, focus & bottlenecks
Single schema joining Git, Jira, Slack, CI, calendar
Business value via features shipped, bugs fixed, SLOs met
Actionable analytics from code, PRs, tickets & tools to drive better engineering decisions
Core metrics: cycle time, PR size, review depth, deploy frequency, MTTR
All-in-one system to track, analyze & optimize engineering performance at scale
Standardized, tool-agnostic metric definitions
Features, bug fixes, refactors delivered to production
PRs merged + tickets closed, weighted by type & size
Measurable developer output balancing speed, quality, impact & sustainability
Automated weekly/monthly reports on KPIs, trends & risks for stakeholders
Metrics per headcount, not absolute (e.g. PRs/engineer)
Features completed vs new bugs introduced per sprint
Score (1–5) on process, autonomy, tooling, predictability
Full telemetry into who’s doing what, when, how
Normalized index of velocity, quality & focus (0–100)
Weeks from start to first merged PR at full velocity
Estimated future cost of refactoring vs current velocity
Work item in issue tracker: bug, story, task, epic
From approved idea to first customer use
From incident detection to full customer recovery
Raw events: commit, comment, deploy, meeting join
Objective evaluations using contribution, quality & collaboration metrics
Estimated cognitive load from change size, file count & diff entropy
% of team output from each engineer (normalized)
Number of concurrent open tasks per engineer (limit: 2–3 ideal)
Total volume of PRs, tickets, LOC, comments per engineer/week
Detects deep work blocks, meeting overload, late-night commits, review gaps
Early indicators: silent devs, PR backlog, late merges
