Project Overview
This large-scale project involved developing an application which served two distinct departments — R&D and Manufacturing — each with unique requirements. Initially created for R&D, the application enabled researchers to manually test new product features, collect data for post-processing, and visualize performance as parameters were adjusted.
Dual-Mode Architecture
As the product matured, the application was adapted into an automated test system ideal for low-volume production, but hours of testing and data collection. While the manufacturing department had different needs, the software architecture supported rapid change requests for R&D prototyping while remaining robust and stable for production environments requiring an automated test framework.
Machine Learning Integration
A large amount of data was logged for post-processing, which was analyzed to develop machine learning models aimed at enhancing test automation. These models allowed the system to improve test decisions over time based on historical results, reducing manual intervention and increasing throughput.
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