LOLC Technologies · iPay Digital Payment Platform
iPay: Mobile & API Automation
End-to-end mobile automation for iPay Customer & Merchant apps on Android + iOS, plus REST Assured API suites covering payment gateway, transaction processing, and merchant onboarding, wired into Jenkins for continuous feedback.
Problem
iPay ships fast. Customer and Merchant apps land on both Android and iOS with a regression cadence measured in days, not weeks. Manual regression across both platforms was the bottleneck: expensive to staff, hard to reproduce, and slow to signal a broken payment flow, which for a licensed digital payment platform is the difference between a healthy release and a rollback.
Architecture
A shared Java automation core with two consumers on top of it:
- Mobile suite: Appium driving real devices and emulators, one Page Object Model tree per app (Customer, Merchant) with capability profiles for Android and iOS. Test data isolated per suite so payment flows never cross-contaminate.
- API suite: REST Assured tests targeting the payment gateway, transaction processing, and merchant onboarding endpoints. Response contracts asserted down to error-code granularity so a silent contract change surfaces immediately.
- CI wiring: Jenkins pipeline stages: build → API suite → mobile suite → aggregated JUnit report. Failures gate the merge; successes update the release candidate.
My contributions
- Designed and implemented the Appium framework end-to-end for iPay Customer & Merchant apps across Android and iOS.
- Built REST Assured suites for the payment gateway, transaction processing, and merchant onboarding, the same endpoints the mobile suite depends on.
- Integrated both suites into Jenkins CI/CD so every merge triggers automated feedback within the release window.
- Ran functional, regression, and exploratory testing on frequent production releases; upheld FinTech compliance around digital payment flows.
Outcome
Manual regression on iPay releases collapsed from a multi-day drag into a CI-gated automated pass. Payment-flow defects that used to escape into staging now surface at merge time. The team ships more confidently, and the mobile QA budget went from headcount-limited to velocity-limited.