Streamlining Clinical Trial Data: Overcoming Performance Testing Challenges

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Streamlining Clinical Trial Data: Overcoming Performance Testing Challenges

Veritas Automata Mauricio Arroyave

Mauricio Arroyave

Client Overview and Challenge
A key player’s goal, in the clinical trial data industry, was to consolidate all clinical trial data on a unified platform.
The client faced the challenge of modifying their application, which was implemented using .NET libraries, to accommodate performance testing. These libraries had become deprecated and needed to be replaced. The client required a solution that was both easy to implement and maintain, and was flexible enough to adapt to future challenges.
Technologies Used:
Solution:

Veritas Automata presented three different options to the client for addressing their performance testing needs, which included Gatling, K6, and JMeter. After a thorough evaluation, JMeter was chosen. Yuxi Global then created 11 scenarios, designed to test the application’s performance under different capacity conditions. JMeter’s multiple post-processors and plugins were utilized to generate results in various formats, such as charts, plain text, or CSV files for more comprehensive reporting.
Client Satisfaction and Testimonial
“Generally, I am wary of putting my trust fully in an outsource partner, especially for a complete function like testing. As a software vendor, it’s critical to ensure that your product is effectively tested before rolling it out to your clients; missed bugs or issues can drastically impact the trust your customers have in you and your overall reputation. Working with Veritas Automata has alleviated those fears. From the first day we engaged both the team and the management, they have been both professional and diligent in managing our software testing as if it is their own product.

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Veritas Automata Intelligent Data Practice

Thought Leadership
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01. Traditional Machine Learning – Learning from Data

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02: Generative AI – Creating the New from the Known

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03: Key Differences Between Traditional Machine Learning (ML) and Generative AI (GenAI) and How to Choose

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