Client’s Need:
Our client, a leading software product company that develops and deploys cutting-edge products for UK police forces, recently re-engineered their classic product, ‘CONNECT’. Before they could deploy it to one of the largest UK police forces, they needed to train end-users (police officers) on the re-engineered product. To accomplish this, our client needed test data loaded into the product for the end-users to use during their training sessions.
Previous attempts to use Robotic Process Automation (RPA) and an automation script approach with Java and Selenium had failed due to the complexity of the product and the associated costs.
Challenges:
The client required the data in the application for training purposes before the product was ready to be used, while it was still evolving. Automating the process of uploading this vast amount of data through the application was a daunting task, as the scenarios provided by the client were outdated when compared to the evolving product.
The performance of the application was inconsistent, making it a challenge to write scripts that could successfully upload data to the product.
The product is massive and extremely complex and learning about it within a short period without proper knowledge transfer was a significant obstacle for end-users.
Solution:
Technossus stepped up to the challenge and offered a solution to deliver data upload within the expected timelines. They assembled a team of experts in Selenium with C# skills. The required scenarios were collected from the client by Technossus’ team, who then adopted an agile approach – explore, learn, and execute. As there was no previous knowledge transfer, Technossus proactively raised the issue with the client team to get feedback and clarification in an agile manner. The team wrote an automation script for each scenario provided by the client, for a total of 12 scenarios. Azure VMs were used for each script to create a staggering amount of test data for each scenario in the product.
As the application was inconsistent, detailed logs were used to identify failing cases and to develop failover plans. Robust scripts were built to tackle failing scenarios and ensure 100% success and accuracy.
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