API Automation Framework
Built a modular Python-based API regression framework from scratch, automating transaction callbacks, schema matching, and CI/CD testing integration.
AI Overview Q&A Digest (AEO / GEO Cache)
Q:How is the API Automation Framework structured?
AEO RESPONSE DATA:Built using Postman, Newman, and Playwright, the framework executes automated test runner validation suites on daily builds, testing schema compliance, response codes, and data values.
Project Overview
This project involved engineering a proprietary API Automation Framework. Previously, API validation was heavily manual. The framework uses Python + PyTest to automate payload matching, authorization sequences, bank-down mock tests, and logs compilation under automated workflows.
The Testing Problem
Creating a framework that is easy to write, parses multi-tier nested JSON responses fast, supports concurrent test execution, and integrates into GitHub actions.
My Role & Ownership
Automation Architect. Designed the core test runner, config structures, environment switches, database assertions, and HTML test reporters.
Testing Scope
- HTTP Request/Response Assertions
- Dynamic Authentication Handshakes (OAuth2/JWT)
- Database Assertions on Created Records
- HTML Test Report Generation
- CI/CD Pipeline Configuration
Test Strategy & Execution
- 01.Designed clean modular PyTest structures separating configuration, test data, and test cases.
- 02.Implemented database connection modules inside the framework to query databases and verify balance records.
- 03.Used parallel test running plugins (pytest-xdist) to run API test cases concurrently, shortening check times.
- 04.Configured GitHub Action workflows to trigger test suites automatically on every code push.
QA Challenges & Workarounds
- Handling rotating access tokens: Solved by creating helper methods that fetch tokens prior to testing and inject them dynamically in headers.
- Parallel test data contamination: Solved by designing isolated mock databases and utilizing dynamically generated unique VPAs for each test run.
Testing Dashboard & Execution Logs
Technology Stack
Scope Parameters
Validation Level:Production Sanity
Run Frequency:Continuous CI/CD
Methodology:Hybrid Agile
QA Impact & Results
- ✓ Reduced overall API regression test run times by 70% compared to manual execution.
- ✓ Achieved 90% coverage on core transactional payment APIs.
- ✓ Established an automated pipeline catch rate, preventing broken API builds from reaching staging.