QA for a Medical Brain Activity Analysis Platform

Challenge

A US-based medical platform analyses brain activity data from specialist hardware devices and generates diagnostic reports for physicians. The platform runs as both a web application and an iOS app — both used in clinical settings where data accuracy and reliability are non-negotiable.

When we joined, the QA function was a single tester doing manual checks only. Test documentation existed only for already-delivered features, making it impossible to assess real coverage or prevent regressions when new functionality shipped. Automation was completely absent. The bug-fixing process was unstructured, with no systematic defect tracking.

For a platform that doctors rely on for diagnosis and treatment decisions, this was a serious risk.

Solution

We provided one QA automation engineer. The brief: high-quality coverage across web and iOS, early defect detection, and a foundation for automation.

Building the QA foundation

• Developed 400+ test cases covering functional, regression, and exploratory scenarios
• Introduced test case management to systematise coverage and enable tracking over time
• Formalised the defect lifecycle — structured reporting, clear priorities, blocking/critical bug flagging
• Developed a cross-platform testing strategy covering both web and iOS to a consistent standard

Manual and API testing

• Conducted thorough functional, regression, smoke, and exploratory testing across both platforms
• Manual API testing via Postman and Swagger — verifying all endpoints and integration documentation
• Configured Postman Flows to automate repetitive API testing scenarios
• UI/UX and accessibility testing on both web and iOS builds
• Cross-platform testing across devices and browsers via BrowserStack

Reporting and process transparency

• Daily testing reports during active cycles
• Final regression reports before each release
• Full defect reports with descriptions, priorities, and reproduction steps
• Zephyr analytics integrated with Jira for defect tracking across the team
• PDF summary reports for stakeholders

Automation groundwork

• Identified key scenarios for future automation
• Configured a framework, reporting pipeline, and CI/CD integration via GitLab
• Initial automation deployed — coverage growing with each sprint

Tools used: Postman, Swagger, Zephyr, Jira, Webdriver.io, Appium, Allure, BrowserStack, GitLab, VS Code

Results

A medical platform with minimal QA discipline became one with structured, traceable, continuously improving quality processes:

• 4–5× reduction in bugs reaching production — measurably safer releases for clinical users
• 226 defects identified and fixed before release — errors resolved before they reached doctors
• 400+ test cases developed from scratch — all key clinical scenarios documented and repeatable
• Cross-platform coverage established across web and iOS
• Full defect visibility for development team — blocking issues escalated immediately
• Automation framework in place and growing — manual testing share decreasing sprint by sprint