QA Engineering Assessment · REM Waste

REM Waste Booking Flow
Platform

Full-stack booking application built and tested solo in 5 days using AI-first engineering approach.

Next.js · TypeScript · Playwright · Vercel

✅ Live on Vercel 🔄 CI/CD Green 35+ Test Cases 2 E2E Flows 5-Day Sprint AI-First Delivery

About This Project

The Challenge

REM Waste issued a QA Engineering Assessment requiring candidates to both build and comprehensively test a realistic booking platform — simultaneously acting as developer, QA engineer, and delivery manager.

The constraint: 5 days, solo delivery, zero infrastructure budget.

This project demonstrates that an experienced QA Architect can own the full engineering cycle — not just test what others build.

The Approach

AI-First Engineering: Claude Code was used as the primary accelerator — not a replacement for judgment. Every AI-generated artefact was reviewed, verified, and often improved before commit.

24 AI-assisted tasks logged in DOC-008 (AI Engineering Log):

  • 4 accepted as-is (17%)
  • 16 accepted with engineer edits (67%)
  • 1 required multiple iterations (4%)
  • 0 rejected

14 hours of estimated engineering time saved.

Project Documentation Library

Complete engineering artefacts — from requirements to release. All authored before or during development, not retrospectively.

Governance
DOC-001
Project Glossary
65 terms across domain (waste management), technical (stack), and process (artefacts) layers. Source of truth for all terminology — every other document references this.
↓ .docx
DOC-002
Project Charter
PMI-compliant. Scope, objectives, constraints, risk register (6 risks), stakeholder register, 5-day sprint timeline. Authored day 1 before any code.
↓ .docx
DOC-003
Product Backlog
40+ user stories with MoSCoW prioritisation, WSJF scoring, acceptance criteria, WBS (functional + architectural), DoR/DoD, sprint plan. Source of truth for scope.
↓ .xlsx
Architecture
DOC-004
ADR Collection
6 Architecture Decision Records: framework selection, deployment platform, CI/CD, test automation tool, reporting strategy, AI engine. Immutable decision log.
↓ .docx
DOC-007
Tech Stack Evaluation
Scored matrices (1–5 per criterion) for 5 technology categories. Objective justification for every stack choice — not "I know this tool" but "this scores highest for these constraints".
↓ .docx
Quality Engineering
DOC-005
Test Strategy
Testing pyramid with coverage targets, risk-based prioritisation table, 10 NFR with acceptance criteria, CI/CD quality gates, AI-augmented testing approach, defect management. Authored before development.
↓ .docx
DOC-009
Security and OWASP Check
Static code analysis for most-common threats and security testing.
↓ .docx
DOC-010
Manual Testing Scripts
Step-by-step test scripts for manual exploratory testing sessions. 10 core scenarios with setup, execution, expected results, and bug logging instructions.
↓ .xlsx
Delivery & AI
DOC-006
Release Management Plan
Branching strategy, 3-environment map (local/preview/production), 11-step PR workflow, 3-tier release checklist, rollback SOP (target: <5min MTTR), DORA metrics baseline.
↓ .docx
DOC-008
AI Engineering Log
24 logged AI-assisted tasks across 5 days. For each: prompt intent, AI output summary, manual changes applied, outcome (accepted/edited/iterated/rejected), verification method, time saved estimate.
↓ .xlsx

Tech Stack

Every tool selected through scored evaluation matrices — see DOC-007 for full justification.

⚛️
Next.js 14
Full-stack framework — FE + API monorepo
🔷
TypeScript
Type safety across app and test layers
🎨
Tailwind CSS
Utility-first styling — rapid UI iteration
🎭
Playwright
UI · API · A11y · Visual — single test ecosystem
⚙️
GitHub Actions
CI/CD — TypeScript gate + smoke tests on every PR
🚀
Vercel
Zero-config deployment — preview + production
🤖
Claude Code
AI engineering accelerator — 14h saved
🟢
Node.js 24
Runtime — LTS, aligned with CI environment
axe-core
Automated accessibility testing in Playwright
Evgenii Subbotin
QA/SDET Lead · QA Architect · SAFe RTE

20 years in software engineering. This project demonstrates AI-first full-cycle delivery — from product spec to production in 5 days.