Purpose: How to test the end to end customer life cycle journey faster and smarter without having a dependency with interfacing systems.
In the agile world and In the complex dependent end to end system, it takes more time to go to market. Product testing with new changes takes more time to complete the testing in a shorter window.
Frequent releases affecting live production stability and at the same time we want to push the changes fast to be competitive.
Delayed delivery of front end component shortens the end to end test cycle.
Identify the customer lifecycle journey across multiple touch points from Analytics tools.
Integrate the customer lifecycle journey through Rest API calls between the dependent systems.
Supplement the API integration validation with Python & BDD automation framework, and integrate with CICD pipeline for continuous validation.
Implement the Stub / mock platform if any of the live API is not ready through Python as it has robust in-built methods.
Enable Spherical Shift – Code coverage & Analysis to find the defects through the code.
This same integrated Python framework can be triggered into multiple instances and injected with more load to assure performance testing.
A light weight Flask GUI application / Jenkins acts as a triggering point for running functional and performance test along with report generation.
A jar library containing re-usable classes aligned with Development framework is created for basic functions like making Web Service call & Assertions.
Test scenarios are built on this framework importing the library classes.
Thus created scenarios are easily integrated into Dev, Ops or E2E team’s suites. Expanded into multiple environments and data centers for easy validation.
Accelerate the testing efficiency through technology transformation
value to business stakeholders through customer experience alignment as we are getting the pattern from Analytics.
Execution of E2E customer flows through automation in a few minutes which drastically reduces 80% of the time.
Ease of mocking/virtualization of API Services and DB layer
Continuous code verification through pipeline process.
More Defects identified in the Development stage (70%) itself through in-sprint automation
Enabled end to end automation including tier down services to identify root cause on the failures.
Automated optimized production like E2E scenario pack through Log Monitoring tool and Analytics
Test E2E scenario early in cycle on any micro-services or DB changes through stubbing and virtualization
Resiliency and Negative scenario testing which is not feasible through UI testing
Superior end-customer experience through consistent experience across devices & platforms