End-to-End QA Automation for a Retail and Warehouse Management Platform
A dedicated QA team built a Playwright automation framework, API checks and a CI pipeline across point-of-sale, inventory, purchasing and warehouse modules, cutting regression testing time by 70%.
REGRESSION TESTING TIME VS. BASELINE
100%30%
At a glance
- PRODUCT
- Retail, POS and warehouse management software
- SCOPE
- Sales and POS, purchasing, inventory, warehouse, reports
- PLATFORMS
- Web application and mobile app (Android and iOS)
- ENGAGEMENT
- Dedicated QA team, time and material
WHERE WE STARTED
The challenge
High regression effort on every release
Each release repeated a large manual regression pass before it could ship.
Complex workflows across modules
Point of sale, inventory, purchasing, warehouse and reports depend on one another.
Frequent integration issues
Problems appeared between modules and with third-party shipping and payment systems.
Limited coverage of critical scenarios
The business flows that matter most were not tested consistently.
Slow multi-device and browser checks
Covering every browser and device combination by hand was time-consuming.
THE SOLUTION
How the QA was built
- 01
Analysed the modules and workflows
A close read of each module and its business workflows before any test was written.
- 02
Automated the critical end-to-end flows
A Playwright and TypeScript framework covering sales, purchasing, inventory, warehouse and reports.
- 03
Added API testing
Postman checks validate the backend directly, so integration issues surface below the user interface.
- 04
Connected it to CI and reporting
Jenkins runs the suites automatically and Allure reports show results; TestRail holds the test management.
- 05
Covered browsers, devices and responsive layouts
Cross-browser and cross-device checks for the web and mobile apps.
- 06
Measured performance on key transactions
JMeter tests on the transactions the business depends on.
Scope covered
The modules and surfaces the automation and API suites cover.
- Sales and POS
- Purchasing and vendor RFQ
- Inventory management
- Warehouse management
- Customers and vendors
- Reports and analytics
- Shipping and payment integrations
- Web application
- Mobile app (Android and iOS)
WHAT CHANGED
Results
85%
AUTOMATION COVERAGE OF CRITICAL WORKFLOWS
70%
REDUCTION IN REGRESSION TESTING TIME
60%
EARLY DETECTION OF DEFECTS IN THE CYCLE
Faster
RELEASES, WITH MORE CONFIDENCE IN EACH ONE
AI-DRIVEN QA
How AI-driven QA extends this setup
Where AI-assisted quality engineering strengthens a framework like this. Every AI suggestion is checked by a QA engineer before it affects a release.
Test generation from requirements
AI drafts Playwright scenarios from module requirements and user flows; an engineer reviews each one before it joins the suite.
Self-healing locators
When the interface changes, AI proposes updated locators so the suite needs less manual repair.
Risk-based regression selection
Change history and defect trends decide which regression flows run first on each build.
Faster failure triage
AI groups failures from the Allure reports so engineers start from likely root causes, not a long list.
Services involved
Tools and technologies
- Playwright
- TypeScript
- Postman
- Apache JMeter
- Jenkins
- Allure Report
- TestRail