QualityCommand

Enterprise QA Management & Release Governance Simulator

Manage shared QA capacity. Evaluate release risks. Make quality decisions visible.

Overview · Business scenario · How to use · Technical architecture · Model boundaries

---

Overview

QualityCommand is a standalone interactive enterprise QA management simulation. It models shared QA staff supporting four concurrent releases, with functional and automation workload estimates, test coverage, defect exposure, quality gates, and executive readiness reporting.

Important

All organizations, projects, staffing numbers, test metrics, defect states, and calculations are fictional. Simulated decisions do not represent completed testing or authorization to release software.

Business scenario

A QA organization has 4 QA leads, 9 functional testers and 4 automation specialists available to support four concurrent enterprise delivery workstreams:

Workstream	Time remaining	Key exposure
Policy Administration Upgrade	4 weeks	Functional workload, test coverage and missing UAT sign-off
Customer Self-Service Portal	3 weeks	Automation workload and cross-team capacity trade-offs
Identity & Access Platform	5 weeks	Open Sev-1 defect, coverage and outstanding UAT
Financial Services API Modernization	3 weeks	Functional capacity and Sev-2 threshold

The goal is to allocate limited QA resources responsibly, evaluate delivery interventions, and produce a transparent release-readiness assessment that identifies unresolved gates rather than hiding them.

How to use

1. Portfolio: Review each project's release window, existing coverage, test-pass rate, defect position, and capacity shortfall.
2. Capacity: Use the +/− controls to reassign QA leads, functional QA, and automation QA. Watch the global staffing pool: overallocations block readiness.
3. Decisions: Choose a workstream and select interventions such as risk-prioritized regression, automation acceleration, controlled scope deferral, or a defect-resolution swarm.
4. Release governance: Inspect the resulting work estimates, coverage changes, capacity gaps, approval obligations, and remaining defect gates.
5. Export: Download the executive brief or JSON scenario, or print the report.

Decision interventions

Decision	Modeled benefit	Important condition
Risk-prioritized regression	Reduces manual effort by 15%	Lowers modeled coverage five points; documented exclusions required
Automation acceleration	Reduces automation effort by 20%	Adds 20 hours of manual validation and review
Defer low-risk scope	Reduces manual effort by 20%	Formal change control and sponsor approval required
Defect swarm	Models removal of one Sev-2	Adds 60 hours; closure remains unverified until tested

Release-readiness model

The engine calculates estimated available testing hours as assigned people × productive hours/week × remaining weeks. It compares capacity with estimated remaining work by discipline, and evaluates sample release criteria:

• No open Sev-1 defects; no more than two modeled Sev-2 defects.
• Test coverage at least 90% and observed pass rate at least 95%.
• Approved business UAT, assigned QA lead, and no staffing over-allocation.
• No unresolved workload gap or unapproved scenario intervention.

If a gate is violated, the workstream is marked NOT READY. Even when modeled minimums are met, the outcome is CONDITIONAL REVIEW, not automatic authorization.

Technical architecture

flowchart LR
  UI[Browser UI] --> APP[app.mjs / interactive decisions]
  APP --> ENGINE[engine.mjs / capacity and gates]
  APP --> EXPORT[Executive brief and JSON]
  TEST[Node tests] --> ENGINE
  SERVER[Python web server] --> UI

Loading

File	Purpose
index.html, styles.css	Responsive dashboard and accessible controls
app.mjs	Browser state, resource assignments, scenario decisions and exports
engine.mjs	Pure deterministic allocation, effort, coverage and readiness calculations
tests/engine.test.mjs	Automated regression tests for capacity and release-gate behavior
server.py	Zero-dependency Python web server for hosting
.github/workflows/test.yml	Test execution on pushes and pull requests

Model validation

The current suite contains 16 automated tests, including an exhaustive 16-combination check for each workstream's four decision options. Tests cover resource shortages, overallocation, defect gates, coverage impacts, scope approvals, and state isolation.

Model boundaries

This is a deterministic simulation intended to make management trade-offs explicit. It does not model workforce skills and availability by day, regulatory certification, vendor constraints, defect arrival rates, calendar holidays, cross-project synchronization, stochastic release risk, or actual project delivery commitments.

Remaining test work estimates and capacity assumptions are editable only through the source model in this version; the live UI allows changes to assignments and interventions. Defect-reduction choices are hypothetical until independently validated. This tool does not replace an authorized quality-management system or release board.

---

QualityCommand · Enterprise QA portfolio management, capacity planning and release governance

_{Independent fictional project · No confidential client or organizational data}