Not able to scale our Client’s on-premise solution which was reaching end-of-life support.
Current system was spread across multiple smaller applications, this providing complexity in testing and performing root cause analysis.
New version was deployed to offer cloud-based options, which was able to scale on demand.
Identify and remove deprecated product functionality testcases from the existing suite.
Increased test coverage from 70% to 90% by adding and executing negative and out of box scenarios.
Regression testing time was reduced from 6 weeks to 1.5 weeks, thus saving 75% of the time taken and enabling faster releases in future.
Our Client is a leading biopharma giant specializing in developing vaccines and specialty general medicines dealing with both prevention and treatment of diseases worldwide.
They have global R&D centers in the US, the UK, Belgium and Italy.
At the start of the engagement, our Client identified several critical challenges that needed addressing to enhance their operations and customer satisfaction. Firstly, the current on-premises version of their platform was not scalable, which limited their ability to grow and adapt to increasing demands. Additionally, this version was nearing its End of Life (EOL) as announced by the platform vendor, meaning that support and updates would soon be unavailable, putting their systems at risk.
Another significant issue is the complexity of their current systems, which is dispersed across multiple smaller applications. This fragmentation complicates testing processes and hinders efficient root cause analysis when issues arise. Consequently, these challenges contribute to a limited feature set available to our Client’s customers, as the older platform version did not support newer functionalities that could enhance user experience and operational efficiency.
Furthermore, our Client was facing substantial costs due to drug wastage at sites and depots. The current system’s inefficiencies lead to drugs being quarantined and expiring, thereby increasing expenses for our Client and their customers. Addressing these challenges was crucial to improving our Client’s system’s reliability, scalability, and feature set, ultimately reducing costs and enhancing service quality for their customers.
The existing test suite from our Client was limited, as it only covered high-level, happy path scenarios while missing critical negative and out-of-the-box workflows. This gap in testing coverage further exacerbates the challenges we face, as it prevents a thorough assessment of the system’s robustness and its ability to handle unexpected or edge-case situations effectively.
The following was implemented:
Qualitest was brought in to test the new version of the application, implement quality engineering best practices and oversee the implementation process. This was planned out in 2 concise phases:
The regression testing phase
This step saved significant go-to market time as this phase was done while the new features were being planned and implemented by the development team. This involved training the team with existing artifacts to identify gaps and update the regression suite.
New Integrations implementation & Randomization improvements
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