The client is an international corporation specializing in track & trace process implementation, which ensures end-to-end supply chain monitoring and traceability.
Challenge
The client wanted to simulate and test their mobile application designed for their clients, in real-life conditions without manual interaction. The client also wanted to establish a QA process that could be a part of the customer’s ecosystem that proactively identifies errors before they resulted in P1 (Priority 1) incidents.
Solution
The Orion team developed a QA process using a manual and automated (Apium) approach to:
-Categorize erroneous data
-Identify patterns leading to process failures
-Inspect the QA environment to validate the process in production
The team also automated the bug reporting system throughout the entire product lifecycle and recommended improvements in the existing system based on bugs identified during usability tests.
By combining big data within the QA process, the end-to-end testing workflow and methodology (including reporting, metrics, and KPIs) helped the client automatically simulate every possible end-user action using Katalon Studio + Java. The Orion team also created re-usable blocks of automated tests, enabling the client to customize test cases.
Result
The implemented automated QA process proactively identified gaps in the client’s business process and functionality. Within a year of implementation, errors were reduced by 70% and P1 incidents were reduced by 80%. Orion’s module-based automation scenarios covered more than 95% of track & trace E2E test cases, driving improved logistics as errors were identified in advance.
Tools:
- Grafana
- Superset analytical tools
- Python
- Postman
- Orchestrated SQL queries
