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, with hours of testing and continuous data collection. While the manufacturing department had different needs from R&D, the software architecture supported rapid change requests for R&D prototyping while remaining robust and stable for production. This is exactly the value of a configurable test system architecture — one codebase, two operating modes, no forked branch — and the same pattern underpins the scalable test system architecture we apply when a project is built for the long haul.
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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