Glossary

AI Assistant: Chat interface for managers: “Show me top blockers this week”
AI-Generated Insights: LLM analyzes PRs, comments & patterns to suggest improvements
Aligned with Roadmap: Work contributing to quarterly OKRs or strategic initiatives
Attrition Rate: % of engineers leaving voluntarily per year (target: <10%)
Bug: Functional defect in production or pre-release code
Calendar: Scheduled meetings reducing available focus time
Change Failure Rate: % of production deploys that cause incidents or rollbacks
Change Throughput: PRs merged per week, normalized by team size
Circle CI Competency Framework: Levels 1–5 in pipeline design, testing, security, observability
Clear Guidance: Specific, metric-driven recommendations for managers to coach effectively
Collaboration Baseline: Expected review depth, response time, cross-team PRs
Comment: Feedback left on PR diff, ticket thread or design doc
Continuous Feedback: Regular, data-informed 1:1s & async feedback loops to improve performance
Continuous Improvement: Small, frequent process tweaks backed by data
Continuous Improvement Culture: Team norm: retros, experiments, kaizen events
Core Competences of a Developer: Coding, system design, testing, collaboration, ownership
Customer Satisfaction Score (CSAT): Post-release survey: “How satisfied are you?” (1–5)
Cycle Time: From task start (e.g. “in progress”) to done (merged/deployed)
Daily Standup: 15-min team sync: what I did, will do, blockers
Data-Backed Insights: Conclusions drawn from unified Git, Jira, CI/CD & calendar data
Data-Driven Culture: Decisions based on metrics, A/B tests, not opinions
Deploy Time: Duration from merge to production (CI/CD pipeline runtime)
Developer Focus Time: Uninterrupted deep work blocks (ideal: 4+ hrs/day)
Developer Productivity Metric: Composite score: output × quality × velocity × sustainability
Development Time: Hours from “in dev” to “ready for review”
Effort Allocation: % time on features, bugs, tech debt, support, learning
Empowered Managers: Given data, budget & authority to fix team issues fast
Engineer Net Promoter Score (eNPS): “How likely to recommend this team?” (-100 to +100)
Flow Time: End-to-end duration from idea to value in production
Hotfix Frequency: Number of urgent post-release fixes per sprint (target: <1)
Incident Resolution Time: Total time from detection to permanent fix deployed
Incident Response Time: Minutes from alert to first engineer action
KTLO: “Keeping The Lights On” – operational, support & maintenance work
Large Language Models: GPT-like models power summaries, risk detection & natural language queries
Lead Time for Changes: Time from first commit to production deployment (DORA metric)
Live Dashboards: Auto-refreshing UIs showing current team state
Live View of Work: Real-time Kanban of all in-flight PRs and tickets
Manager Alignment: % of time on strategy, coaching vs firefighting & admin
Manager Context Switching: Number of meetings + Slack pings per day disrupting flow
Mean Time Between Failures: Avg uptime between production-breaking incidents
Mean Time to Recovery: Avg duration to restore service after production incident
Merge Time: Hours from final approval to actual merge into main branch
New Features: User-facing functionality delivering product or business value
Onboarding Baseline: Standard checklist + metrics for new hire integration
One-on-One: Weekly 30-min manager-engineer sync on goals, blockers, growth
Operational Maturity Level: Stage 1–4: chaotic → reactive → standardized → optimized
Pensero: AI-powered platform unifying dev metrics, AI insights & management tools for engineering leaders
Performance Improvement Program: 30–90 day plan with metrics to address performance gaps
Performance Management: Ongoing cycle: set goals → track → review → improve
Performance Measurement: Systematic, fair tracking of individual & team contributions over time
Performance Review: Quarterly/annual formal assessment of impact & growth
Privacy-First Platform: Anonymized data, role-based access, no PII in analytics by default
Proactive Support: AI flags at-risk engineers or processes before issues escalate
Pull Request: Proposed code change submitted via Git for review and merge
Pull Request Review: Peer code inspection with comments, approval or request changes
Pull Request Size: Total lines added + deleted; small = <200, large = >1000
R&D Activation: % of engineering time on innovation vs maintenance
Real-Time Insights: Live alerts on stalled PRs, high WIP, low focus time or rising tech debt
Release Frequency: Average deploys per week (elite teams: multiple per day)
Reporting for Everyone: Self-service dashboards for devs, PMs, designers
Retention of Engineers: % of devs still employed after 12 months (target: >90%)
Review Cycles: Average rounds of feedback per PR before approval
Review Depth: Average comments per 100 lines changed in PR
Review Load: Number of open PRs awaiting review per active reviewer
Review Participation Rate: % of team members who reviewed at least one PR this week
Review Time to Merge: Median hours from first review to final merge (excl. author fixes)
Rework: Post-merge fixes due to bugs, review misses or scope changes
Service Level Agreement (SLA): Contractual commitment to customers (e.g. 99.95% availability)
Service Level Objective (SLO): Internal reliability target (e.g. 99.9% uptime)
Software Engineering Analytics: Statistical models on velocity, quality, burnout risk
Software Engineering Dashboards: Real-time, customizable views of team velocity, quality, focus & bottlenecks
Software Engineering Data Unification: Single schema joining Git, Jira, Slack, CI, calendar
Software Engineering Impact Metric: Business value via features shipped, bugs fixed, SLOs met
Software Engineering Intelligence: Actionable analytics from code, PRs, tickets & tools to drive better engineering decisions
Software Engineering KPIs: Core metrics: cycle time, PR size, review depth, deploy frequency, MTTR
Software Engineering Management Platform: All-in-one system to track, analyze & optimize engineering performance at scale
Software Engineering Metrics Foundation: Standardized, tool-agnostic metric definitions
Software Engineering Output: Features, bug fixes, refactors delivered to production
Software Engineering Output Measurement: PRs merged + tickets closed, weighted by type & size
Software Engineering Productivity: Measurable developer output balancing speed, quality, impact & sustainability
Software Engineering Reporting: Automated weekly/monthly reports on KPIs, trends & risks for stakeholders
Software Engineering Teams Scale: Metrics per headcount, not absolute (e.g. PRs/engineer)
Story to Bug Ratio: Features completed vs new bugs introduced per sprint
Team Maturity Metric: Score (1–5) on process, autonomy, tooling, predictability
Team Observability: Full telemetry into who’s doing what, when, how
Team Productivity Score: Normalized index of velocity, quality & focus (0–100)
Team Ramp-up Time: Weeks from start to first merged PR at full velocity
Technical Debt Ratio: Estimated future cost of refactoring vs current velocity
Ticket: Work item in issue tracker: bug, story, task, epic
Time to Market: From approved idea to first customer use
Time to Restore Service: From incident detection to full customer recovery
Tool Activity Data: Raw events: commit, comment, deploy, meeting join
Transparent Performance Reviews: Objective evaluations using contribution, quality & collaboration metrics
Work Complexity: Estimated cognitive load from change size, file count & diff entropy
Work Contribution: % of team output from each engineer (normalized)
Work in Progress (WIP): Number of concurrent open tasks per engineer (limit: 2–3 ideal)
Work Magnitude: Total volume of PRs, tickets, LOC, comments per engineer/week
Work Pattern Analysis: Detects deep work blocks, meeting overload, late-night commits, review gaps
Work Signals: Early indicators: silent devs, PR backlog, late merges